Abstract

This study examines the challenges and opportunities presented by equity, inclusiveness, and the digital divide in the adoption of Artificial Intelligence (AI) within Technical and Vocational Education and Training (TVET) institutions in Zimbabwe. The study adopted mixed methods design, employing quantitative surveys to map infrastructural and device equity disparities (the digital divide) and qualitative critical ethnography. It used a sample size totalling 5 TVET institutions and 50 key informants. Quantitative data was analysed using frequency distributions to map the prevalence of infrastructural disparities and regression analysis to determine the statistical significance of access gaps related to socio-economic factors. Qualitative data was analysed using thematic analysis to identify, analyse, and report patterns (themes) within the interview and focus group data. Findings revealed significant disparities in AI readiness, with urban institutions generally better equipped. Challenges such as limited funding and high internet costs disproportionately affect marginalized groups. The study concludes that while AI has transformative potential for TVET, equitable adoption is obstructed by systemic inequities and the digital divide. Key recommendations include investing in digital infrastructure, creating national AI strategies for TVET, integrating digital literacy into curricula, and fostering public-private partnerships for AI training.

Keywords: Equity, inclusiveness, digital divide, Artificial Intelligence, TVET. 

1        Introduction and Background

The burgeoning potential of Artificial Intelligence (AI) to revolutionise Technical and Vocational Education and Training (TVET) in Africa, particularly through adaptive learning systems or intelligent tutoring bots, is frequently championed for its capacity to address skill gaps and enhance learning outcomes. However, this research posits that the actualisation of such transformative benefits is profoundly contingent upon, and often hampered by pervasive issues of equity, inclusiveness, and the enduring digital divide. The main objective of this study is to investigate how these socio-technical disparities influence the rate, nature, and successful integration of AI adoption within Zimbabwe’s TVET sector. Equity here refers to the fair and just distribution of access to AI-enabled educational resources and opportunities. Inclusiveness extends this to active participation and benefit, preventing the marginalisation of specific groups. The digital divide refers to disparities in access to reliable high-speed internet, affordable computing devices, and consistent electricity, directly impeding the deployment of cloud-based AI simulation platforms or real-time AI-driven diagnostics essential for modern TVET curricula. Consequently, the adoption of AI in TVET, encompassing the strategic integration of AI tools and methodologies into teaching, learning, and administrative processes. This becomes a complex socio-technical challenge rather than a purely technological one in contexts like Zimbabwe, where these foundational disparities are pronounced.

The global landscape for Artificial Intelligence (AI) adoption is characterised by rapid technological advancements and widespread integration across various sectors, including vocational education and training, projected to add $15.7 trillion to the global economy by 2030 (PwC 2017). However, this transformative potential is critically tempered by persistent concerns regarding equity, inclusiveness, and the digital divide. While high-income nations lead in developing and deploying sophisticated AI tools, such as intelligent tutoring systems or AI-powered simulation platforms for TVET, there’s a growing recognition that existing biases in algorithms can exacerbate societal inequalities, for instance, through gender or race-biased hiring algorithms (Buolamwini & Gebru 2018; UNESCO 2019). The global digital divide, despite increasing internet penetration with approximately 66% of the world population online by 2023, remains a significant barrier, particularly concerning access to reliable high-speed broadband, affordable AI-compatible devices, and the digital literacy skills required to navigate and leverage AI technologies effectively (ITU 2023; World Economic Forum 2023). This global disparity critically shapes how TVET institutions can adopt AI to prepare a future-ready workforce, with many struggling to bridge the gap between aspirational integration and practical, equitable implementation due to infrastructure, resource, and policy limitations.

While Africa possesses immense potential for leapfrogging development through AI-driven innovation, its adoption within Technical and Vocational Education and Training (TVET) sectors is largely nascent and heavily constrained by systemic challenges (AfDB 2023). The digital divide is profoundly pronounced across Africa, where despite a 40% internet penetration rate in Sub-Saharan Africa as of 2022, access to affordable broadband, consistent electricity, and digital devices remains a luxury for many, particularly in rural and low-income urban areas (GSMA 2023; World Bank 2022). This fundamental lack of infrastructure severely limits the capacity of TVET institutions to integrate AI tools like predictive analytics for student performance or AI-driven virtual reality for practical skills training. Furthermore, issues of equity and inclusiveness are exacerbated by deeply entrenched socio-economic inequalities, gender disparities in Science, Technology, Engineering and Mathematics (STEM) fields, and a lack of disability-inclusive policies within educational systems (African Union 2022). Consequently, the benefits of AI risking being concentrated among a privileged few, leaving a vast majority of TVET learners, especially women and those from marginalised communities, thus widening the existing skills gap rather than bridging it.

In Zimbabwe, the impact of equity, inclusiveness, and the digital divide on AI adoption within TVET is particularly acute, manifesting as a complex interplay of socio-economic constraints and infrastructural deficiencies. While the government’s “Education 5.0” philosophy theoretically advocates for innovation and industrialisation, including AI integration, practical implementation in TVET remains rudimentary (Ministry of Higher and Tertiary Education, Innovation, Science and Technology Development 2020). The digital divide is stark: high data costs (among the highest in the SADC region), inconsistent electricity supply, and limited access to internet-enabled devices, especially in rural areas where over 60% of the population resides (POTRAZ 2023). This impede access to even basic digital learning platforms, let alone advanced AI applications (ZIMSTAT 2022). This creates a critical barrier for TVET colleges attempting to introduce AI modules, such as machine learning for agricultural automation or AI for predictive maintenance in manufacturing. Moreover, profound inequalities in educational access, exacerbated by economic challenges and historical disparities, mean that substantial segments of the population, particularly women, rural youth, and persons with disabilities, lack the foundational digital literacy and opportunities to engage with emerging technologies (ZIMSTAT 2022). Therefore, the adoption of AI in Zimbabwean TVET faces significant hurdles, risking the creation of a ‘digital underclass’ if equity and inclusiveness are not explicitly prioritised in policy and resource allocation for technological integration.

1.1       Equity in TVET

The impact of equity manifests primarily through profound institutional resource disparities, where limited central government funding fosters a bifurcated system. Well-resourced urban TVET centres (such as Harare Polytechnic) possess the necessary high-specification hardware and stable fibre connectivity required for running AI infrastructure and data science curricula. While, rural and peri-urban colleges contend with obsolete equipment and inconsistent power supply (load shedding), rendering advanced AI tools functionally inaccessible (Mutambara 2022). This structural inequity means AI adoption is geographically and economically determined. 

1.2       Inclusiveness in TVET

Inclusiveness barriers are evident in the critical underrepresentation of vulnerable demographics; specifically, the entrenched gender imbalance in STEM pathways means female participation in AI-enabling fields remains significantly lower, failing to harness a crucial segment of the national talent pool required for AI innovation (Chisikwa & Shava 2021). The lack of culturally relevant or locally contextualised AI learning materials, coupled with insufficient accessibility features for students with disabilities, creates significant pedagogical friction that limits truly broad-based adoption. 

1.3       Digital divide in TVET

The pervasive digital divide acts as the multi plier of these inequalities. While, AI relies heavily on cloud computing, high-speed data transfer, and costly proprietary software licenses, the majority of marginalised communities in Zimbabwe face prohibitive costs for individual internet access and lack reliable infrastructure backup (UNESCO 2023). For example, attempts to integrate sophisticated AI simulation tools often fail due to the high cost of bandwidth and poor latency in remote areas. Thus, ultimately excluding students from low-income backgrounds from mastering the practical skills necessary for the future AI-driven labour market, effectively reinforcing the chasm between technological aspiration and operational reality in Zimbabwean TVET.

1.4       Objectives of the paper

1. To assess the barriers related to equity and inclusiveness that hinder the effective adoption of AI technologies in TVET institutions in Zimbabwe.
2. To investigate how the digital divide affects access to AI tools and resources among marginalised communities.

2        Methodology

The study adopted mixed methods design, employing quantitative surveys to map infrastructural and device equity disparities (the digital divide) and qualitative critical ethnography. It utilised semi-structured interviews and focus groups to analyse institutional policy failures and perceived barriers to inclusion among marginalized TVET students. This approach is anchored by the critical realism paradigm, which is essential for critically distinguishing between underlying structural causes (such as persistent governmental neglect of rural infrastructure) that generate the digital divide and the observable, socially constructed realities (barriers to AI uptake) within the Zimbabwean context.

2.1       Data collection instruments

This study used dual-instrument design to ensure theoretical saturation (qualitative) and pattern identification (quantitative), aligning with the study’s focus on contextual depth over statistical generalization.

2.1.1      Questionnaire 

The questionnaire was used to map infrastructural and device equity disparities (digital divide) in TVET institutions, measuring tangible gaps in access, resources, and readiness for AI adoption.

Sections of the questionnaire

• Demographic and Institutional profile

This captured context (for example, institution location, participant role, age, gender, disability status).

• Digital infrastructure access

Quantify physical access to technology and internet, for example availability of devices (computers, tablets), internet reliability (scale: “never” to “always”). Frequency of internet access for learning/teaching.

• AI tool accessibility and usage

The purpose of this section was to measure exposure to AI resources and usage patterns, for example, awareness of AI tools (such as ChatGPT, adaptive learning platforms). Frequency of AI tool usage (scale: “daily” to “never”).

• Perceived barriers to equity

This section was used to gauge perceived obstacles to inclusive AI adoption such as agreement levels (Likert scale was used) on statements like resource availability for marginalised students to use AI tools and cost of internet/data on AI training.

• Institutional support and policy efficacy

The section assessed institutional/governmental support for AI equity, for example, satisfaction with institutional AI policies (scale: “very dissatisfied” to “very satisfied”). Availability of AI training programs for staff/students.

In the questionnaire, three measurement scales were used, namely: 

• Nominal: Categorical data (for example, institution location, gender).
• Ordinal: Ranked responses (for example, Likert scales: 1–5 for agreement/satisfaction).
• Interval: Scaled metrics (for example, frequency of internet access: 1 = “never” to 5 = “always”).

2.1.2      Reliability and validity of the questionnaire

Internal consistency was measured via Cronbach’s alpha (target ≥0.7) for multi-item scales (for example, barrier perceptions). Test-retest was piloted with 10 participants; re-administered after 2 weeks to ensure stability (target correlation ≥0.8). Content validity was done by 3 AI/TVET specialists to align items with objectives. Exploratory Factor Analysis (EFA) was used to confirm dimensionality of scales (for example, “infrastructure access” as a distinct factor of construct validity). Criterion validity was correlated with external data (for example national broadband coverage statistics).

2.1.3      Interview protocol for qualitative data

This was used to explore institutional policy failures, lived experiences of marginalisation, and structural barriers through critical ethnography. Semi-structured interviews took 45–60 minutes per interview/focus group. Individual interviews involved institutional heads, policy strategists and IT staff. Focus groups involved students/instructors (6–8 participants per group).

Respondents were asked about 8-10 core questions. The nature of questions requested respondents to describe institution’s current use of AI in teaching/learning. What works, and what doesn’t?”; specific examples of AI tools used; student/instructor involvement; challenges faced by students in accessing AI resources; infrastructure gaps; gender/disability inclusion/exclusion; policy shortcomings; the rural-urban divide impact access to AI tools; Compare experiences; government/institutional policies how they perpetuate or alleviate the digital divide and changes that would make AI adoption more inclusive.

In order to ensure integration with critical realism paradigm, questionnaire solicited for surface observable disparities (for example, device access statistics) and interviews uncovered generative mechanisms (for example, how historical underfunding of rural areas causes digital exclusion). After triangulation of data, quantitative data revealed what was happening in institutions and qualitative data explained through lived realities and power dynamics.

2.2       Population

The target population comprises all institutional heads, instructors, AI policy strategists (Ministry of Higher and Tertiary Education), and students from various publicly funded Technical and Vocational Education and Training (TVET) institutions across Zimbabwe, focusing particularly on those with demonstrable urban-rural resource disparity.

Critical case and maximum variation sampling was adopted to select information-rich cases. This non-probability method is essential to capture the systemic nature of equity barriers by deliberately including institutions facing significant infrastructural challenges (such as rural Industrial Training Centres) alongside well-resourced urban centres (such as Harare Polytechnic), providing critical contrast necessary for assessing the digital divide’s impact.

2.3       Sampling

The sampling was in two phases, that is, quantitative phase and qualitative phase. This was necessary to ensure the depth required for critical quantitative and qualitative analysis. Quantitative phase (survey) was for mapping infrastructural/device equity disparities. Sampling frame showed lists of registered students and employed staff (instructors, administrators, IT staff) within the 5 selected TVET institutions (2 Urban, 3 Rural/Peri-urban), 10 respondents per each institution giving a total of 50 respondents. Stratified random sampling was employed. Strata 1 showed institution location: Urban vs. Rural/Peri-urban and Strata 2 showed role of respondent such as student, Instructor/Educator, Administrator, IT/Technical Staff. Qualitative Phase (Interviews/Focus Groups) involved analysing policy failures and perceived barriers. Target population included key informants with deep contextual knowledge or direct lived experience of barriers to AI adoption such as Institutional Heads, Instructors, AI policy strategists, marginalised TVET students, IT/Technical staff.

Purposive Sampling (specifically maximum variation sampling and criterion sampling) was used. Criterion 1 showed Role/Stakeholder Group (Heads, Instructors, Policy Makers, Students, and IT Staff). Criterion 2: Institution location (Urban vs. Rural/Peri-urban). Criterion 3 showed experience of students and staff with marginalisation/digital divide (especially for students and staff in rural institutions). The study deliberately selected participants to ensure diverse perspectives across locations, roles, genders, and experiences of marginalisation (maximum variation).

The sample size was 50 key informants (22 Urban, 28 Rural/Peri-urban). There were 25 Educators/Administrators (Mix of Heads, Dept. Heads, and Senior Instructors). Students were 20 (Deliberately included students facing marginalisation for example, rural, low-income, women in non-traditional fields, people with disabilities (PWD) and 5 IT/Technical Staff (Key for understanding technical barriers and support)

In order to reach qualitative data saturation, interviews continued until no new significant themes or insights emerged regarding institutional policy failures, structural causes, and perceived barriers, particularly concerning equity and the digital divide. The sample of 50 across diverse roles and locations was deemed sufficient to achieve this depth within the Zimbabwean TVET context for this study.

2.4       Data analysis 

In order to analyse quantitative data, the study employed descriptive statistics, frequency distributions to map the prevalence of infrastructural disparities and inferential methods, regression analysis to determine the statistical significance of access gaps related to socioeconomic factors, such as the significant urban-rural divide in internet connectivity and device ownership.

Qualitative data was analysed using thematic analysis to critically identify, analyse, and report patterns (themes) within the interview and focus group data, revealing how institutional policies systematically fail to address intersectional barriers, such as gendered access limitations and the lack of local-language AI content, which perpetuate exclusion.

2.5       Ethical considerations

The study upheld principles of informed consent by clearly outlining the research purpose and potential risks to participants, particularly those from marginalised communities, and ensured data anonymity to protect vulnerable populations from potential repercussions stemming from their experiences with the digital divide.

3        Presentation of results and discussion

This section presents study findings and discussion. 

Table 1: Overview of sampled TVET Institutions and Key Informants (Interview)

Category	Number	Description
TVET Institutions	5	2 Urban-based, 3 Rural/Peri-urban based institutions.
Key Informants (Total)	50	25 Educators/Administrators, 20 Students, 5 IT/Technical Staff.
Urban Key Informants	22	From urban-based TVET institutions.
Rural/Peri-urban Key Informants	28	From rural/peri-urban based TVET institutions.

The distribution in table 1 above, highlights a slight skew towards rural/peri-urban representation, mirroring the broader geographic distribution of TVET institutions and the focus on understanding disparities.

The following table shows AI readiness factors from the 50 respondents obtained from the 5 TVET institutions.

Table 2: AI readiness factors (Equity & Inclusiveness barriers) – Survey (N=50)

AI Readiness Factor (Likert Scale: 1=Strongly Disagree, 5=Strongly Agree)	Mean	Std. Dev.	Interpretation
Dedicated funding for AI initiatives	1.85	0.92	Critically Low: Indicates a severe lack of dedicated financial commitment, rendering AI adoption an afterthought or an impossibility.
Adequacy of staff AI raining & skills	2.10	1.05	Highly Inadequate: Staff largely untrained, signalling a critical human capital deficit for AI pedagogy and maintenance.
Current curriculum integration of AI concepts	1.70	0.88	Minimal to Non-existent: AI remains largely detached from core TVET curricula, reflecting systemic inertia.
Accessibility of AI tools for Persons with Disabilities (PWDs)	1.55	0.75	Alarmingly Poor: Suggests a complete oversight of inclusive design, marginalising a significant demographic.
Gender equity in AI-related programs/courses	2.90	1.20	Moderate but concerning: While not as dire as other factors, indicates persistent, subtle biases or structural barriers.

From table 2 above, the descriptive statistics paint a grim picture of AI readiness within Zimbabwean TVET institutions. The mean scores, consistently below the midpoint of the Likert scale for most factors, reveal a profound systemic unpreparedness. The extremely low score for “Dedicated funding for AI Initiatives” (M=1.85; SD=0.92) is particularly damning, suggesting that AI integration is not a strategic priority but rather an aspirational, unfunded mandate. Similarly, the “Adequacy of staff AI training” (M=2.10; SD=1.05) highlights a significant human resource gap. Without a skilled faculty, even donated AI tools would remain underutilised or improperly deployed. The near-abysmal score for “Accessibility of AI tools for PWDs” (M=1.55; SD=0.75) is a stark indictment of the sector’s failure to embed inclusiveness from the outset, condemning a vulnerable population to further exclusion from future-oriented skills. Even the “Gender equity” score (M=2.90; SD=1.20), while comparatively higher, still signals the presence of barriers, suggesting AI fields may be inadvertently perpetuating existing gendered occupational segregation.

The following table shows frequency distribution of infrastructural disparities affecting AI adoption (Digital Divide) in Zimbabwe, between urban and rural/peri urban.

Table 3: Frequency distribution of infrastructural disparities affecting AI adoption (Digital Divide) – Key Informants (N=50)

Infrastructural Factor	Urban TVET Key Informants (N=22)	Rural/Peri-urban TVET Key Informants (N=28)	Overall (N=50)	Interpretation
Reliable high-speed internet access	18 (81.8%) Yes, 4 (18.2%) No	5 (17.9%) Yes, 23 (82.1%) No	23 (46%) Yes, 27 (54%) No	Profound urban-rural disparity: While urban institutions fare moderately well, the overwhelming majority of rural key informants lack reliable internet, rendering AI tools inherently dependent on connectivity inaccessible. This is a primary manifestation of the digital divide.
Adequate devices (computers/laptops)	15 (68.2%) Yes, 7 (31.8%) No	3 (10.7%) Yes, 25 (89.3%) No	18 (36%) Yes, 32 (64%) No	Severe device shortage: A critical bottleneck. Even with internet, lack of suitable devices (often outdated or insufficient in number) prevents hands-on AI learning. The rural disparity here is particularly acute, reinforcing the notion of a two-tiered system where advanced learning is reserved for the privileged few.
Consistent electrical power supply	20 (90.9%) Yes, 2 (9.1%) No	10 (35.7%) Yes, 18 (64.3%) No	30 (60%) Yes, 20 (40%) No	Electrification gap: Unreliable power in rural areas negates the utility of any technological investment. Frequent outages not only disrupt learning but also damage sensitive equipment, adding to the cost burden and disincentivising technology adoption. This highlights a fundamental infrastructural failure preceding even digital readiness.
Access to AI-specific hardware (GPUs, specialised labs)	2 (9.1%) Yes, 20 (90.9%) No	0 (0%) Yes, 28 (100%) No	2 (4%) Yes, 48 (96%) No	Virtually non-existent: This is perhaps the most glaring deficiency. AI, particularly advanced applications, demands significant computational power. The near-total absence of specialised hardware across all institutions, and absolutely none in rural settings, demonstrates that AI integration is currently, at best, theoretical and, at worst, an illusion. This reveals a profound lack of investment and strategic foresight.

From table 3, the frequency distributions starkly illustrate the devastating impact of the digital divide. The urban-rural chasm is not merely a gap but an abyss, particularly concerning “Reliable high-speed internet access” and “Adequate devices.” While 81.8% of urban key informants reported reliable internet, a mere 17.9% of their rural counterparts did. This disparity cascades to device availability, where less than 11% of rural key informants have adequate devices. The “Consistent electrical power supply” issue in rural areas is a fundamental barrier that precedes any discussion of digital technology. How can one power devices or access internet consistently without electricity? Finally, the “Access to AI-specific hardware” figures (a paltry 4% overall, and 0% in rural settings) are a critical alarm. This signifies that for most Zimbabwean TVET students, AI remains an abstract concept, utterly detached from practical application, a betrayal of the vocational spirit. This isn’t just a digital divide; it’s a technology infrastructure desert in many crucial areas.

The following table shows impact of socio-economic factors on AI tool access.

Table 4: Regression analysis: Impact of socio-economic factors on perceived access to AI tools & resources (N=50)

Dependent variable: Perceived access to AI tools & resources (Composite score, 1-5)

Independent Variable	Beta Coefficient (β)	Std. Error	t-value	p-value	Interpretation
(Constant)	0.85	0.21	4.05	<0.001	Baseline access is exceedingly low, even before accounting for other factors, indicating a generalised access challenge.
Urban rural (Dummy: 1=Urban, 0=Rural)	1.89	0.35	5.40	<0.001	Highly significant predictor: Being in an urban TVET institution significantly and positively impacts perceived access to AI tools. This confirms the urban-rural divide is not just anecdotal but a statistically robust determinant of access, fundamentally disadvantaging rural learners.
Internet ConnectivityScore (1-5)	0.42	0.12	3.50	0.001	Significant predictor: Self-reported higher internet connectivity scores are strongly associated with increased perceived access to AI tools. This underscores internet access as a crucial gateway, the absence of which renders advanced learning unattainable.
Device ownership (1-5)	0.38	0.11	3.45	0.001	Significant predictor: Higher scores for device ownership/availability are also strongly associated with greater perceived access. This highlights that physical access to appropriate hardware is as critical as connectivity, forming a dual barrier for many.
R-squared	0.72				Strong explanatory power: Approximately 72% of the variance in perceived access to AI tools can be explained by these three factors. This suggests that the urban-rural divide, internet connectivity, and device ownership are the dominant drivers of AI access disparities, rather than tertiary issues. These are fundamental, systemic barriers demanding urgent, targeted interventions.

From table 4 above, the regression analysis provides a statistically robust confirmation of the profound impact of socio-economic factors on AI tool access. The R-squared value of 0.72 is remarkably high, indicating that the urban-rural location, internet connectivity, and device ownership collectively explain a dominant proportion of the variance in perceived access to AI tools. This directly refutes any notion that these are minor obstacles; they are the defining factors.

Specifically, the highly significant positive beta coefficient for “Urban rural” (β=1.89, p<0.001) confirms that urban TVET learners experience significantly higher access to AI tools compared to their rural counterparts. This is not merely a slight advantage but a dramatic difference, cementing the urban-rural divide as a fundamental structural inequity in AI readiness. Similarly, “Internet connectivity score” (β=0.42, p=0.001) and “Device ownership” (β=0.38, p=00.001) are statistically significant positive predictors. This demonstrates that without reliable internet and adequate devices, the aspiration for AI adoption in TVET is nothing more than an imagination. The low constant (0.85) further implies that even assuming average conditions for the independent variables, baseline access is critically low, indicating a broader, sector-wide deficiency.

The adoption of Artificial Intelligence (AI) in Technical and Vocational Education and Training (TVET) in Zimbabwe presents a dual challenge: it is a critical avenue for future economic development, yet its effective integration is profoundly obstructed by entrenched issues of equity, inclusiveness, and the digital divide. The qualitative themes presented below, structured from the study objectives, critically illuminate how existing institutional policies systematically fail to address intersectional barriers, perpetuating exclusion and limiting the transformative potential of AI.

Objective 1 explores how various dimensions of equity and inclusiveness create barriers to AI adoption in Zimbabwean TVET. The themes reveal policy shortcomings that exacerbate these societal inequalities within the educational landscape.

The following table shows the qualitative themes that came out during interviews and how the respondents wish to have them addressed.

Table 5: Qualitative themes on equity and inclusiveness barriers to AI adoption

Theme title	Key concepts/sub-themes	Illustrative data
1. Gendered Disparities in AI Pathways and Access	– Underrepresentation of women in advanced technical TVET programs. – Persistence of socio-cultural norms and gender stereotypes. – Lack of female role models and targeted mentorship.	In rural TVET colleges, girls are often subtly steered towards traditional ‘feminine’ trades like cosmetology, home economics (Food and Nutrition) or garment construction. While, AI, robotics, and coding courses are implicitly marketed to boys. The curriculum and career guidance don’t actively challenge these stereotypes, and there are almost no female lecturers in these advanced technical subjects to inspire female students. When AI tools are introduced, they sometimes reflect inherent biases from their Western, male-dominated development contexts. Female students struggle to connect with these abstract tools, and the local curriculum hasn’t been adapted to make AI relevant and accessible to their unique experiences or local Zimbabwean challenges faced by women entrepreneurs or artisans. Policy: Institutional policies lack explicit targets and proactive strategies for increasing female participation in emerging technology fields, often perpetuating existing gendered divisions through resource allocation, curriculum design, and insufficient support for inclusive pedagogy.
2. Linguistic and Cultural Irrelevance of AI Content & Pedagogy	– Predominance of English language and Western-centric AI curricula. – Scarcity of AI tools and content supporting indigenous languages (such as Shona, Tonga, or Ndebele). – Disconnect between AI use cases and local Zimbabwean economic/social contexts.	Most of the accessible AI learning platforms, tutorials, and open-source tools are exclusively in English. For a significant number of students, especially those from non-urban areas where English proficiency is lower, this is a major cognitive barrier. There’s almost no AI content available that explains complex concepts in Shona, Tonga or Ndebele, let alone applying AI directly to local challenges like improving small-scale agriculture or managing rural health data. TVET curriculum for emerging technologies largely adopts international frameworks without sufficiently adapting AI examples to problems a Zimbabwean artisan, farmer, or small business owner would genuinely face. It remains too abstract, too foreign, making it difficult for students to see the immediate value and practical application of AI in their local context. This lack of cultural resonance significantly hinders engagement and understanding. Policy: National TVET and AI strategies often overlook the critical need for curriculum indigenisation and investment in local language processing for AI. This passive acceptance of global North-derived content perpetuates linguistic and cultural exclusion, failing to empower local innovation or address unique national needs.
3. Socio-economic and disability-related access exclusions	– Financial constraints for marginalised students (for example, data, personal devices). – Physical and digital inaccessibility of AI laboratories and online platforms for students with disabilities. – Lack of supportive infrastructure for diverse learning needs.	Most academically promising students, particularly those from low-income families in high-density suburbs or rural areas, cannot afford the consistent data bundles required to access online AI modules or participate in collaborative AI projects from home. They are wholly reliant on limited, often oversubscribed, on-campus lab time, which simply isn’t sufficient for the depth of learning required. Many computer labs with AI software are not wheelchair accessible, and the AI platforms used are rarely compatible with assistive technologies like screen readers for visually impaired students. There’s inadequate budget allocated for adaptive technologies or pedagogical adjustments for students with diverse learning needs, effectively creating insurmountable barriers to AI education. Policy: Institutional budgets and national educational policies often fail to allocate adequate funds for accessible technology, assistive devices, or data subsidies. This systemic omission implicitly excludes students facing socio-economic hardships.

From table 5, the themes derived from Objective 1 critically reveal that the ambition for AI adoption in Zimbabwean TVET is fundamentally undermined by systemic failures to uphold principles of equity and inclusiveness. The persistence of gendered disparities in AI pathways is not merely a societal reflection but is actively reinforced by institutional neglect. The respondents felt that absence of explicit policies for promoting female participation, the lack of gender-sensitive curriculum design, and the dearth of visible female role models within advanced technical fields demonstrate an institutional inertia that perpetuates a cycle of exclusion. This ensures that a significant demographic is systematically denied access to future-oriented skills. The study found that the cultural irrelevance of AI content and pedagogy represents a profound failure to localise education. Mutambara (2022) also noted that the uncritical adoption of English-centric, Western-developed AI curricula alienates a vast majority of students, particularly those from non-urban and indigenous language-speaking backgrounds. This policy oversight, the lack of investment in local language processing for AI and context-specific use cases, renders AI abstract and inaccessible, hindering its practical application to Zimbabwean development challenges. The study found that the socio-economic and disability-related access exclusions highlight critical intersectional barriers. The absence of institutional provisions such as data subsidies, affordable device schemes, or universally designed learning environments effectively disenfranchises students who are already economically disadvantaged or live with disabilities. These policy gaps demonstrate a lack of commitment to dismantling historical inequalities, instead permitting them to be amplified within the crucial domain of AI education, thereby entrenching a two-tiered system of access and opportunity.

Objective 2 focuses on the tangible impacts of the digital divide, examining how disparities in infrastructure directly impede access to AI for marginalised communities within and around TVET institutions in Zimbabwe.

The following table shows digital divide’s impact on AI access in Zimbabwe, urban and rural/peri urban institutions. 

Table 6: Qualitative themes on digital divide’s impact on AI access

Theme title	Key concepts/sub-themes	Illustrative data
1. Inadequate Digital Infrastructure in Underserved TVET institutions.	– Limited or unreliable internet connectivity (slow speeds, frequent outages). – Insufficient number of functional and AI-capable devices per student. – Lack of consistent and reliable electricity supply in urban or remote institutions.	In many TVET colleges located in urban or rural areas of Zimbabwe, internet connectivity is slow and frequently drops, making it impossible to stream AI tutorials, access cloud-based development environments, or download necessary AI libraries. When there’s load shedding, which is often, no practical AI learning takes place. There is an outdated computer lab with 25 aging machines for over 400 enrolled students. These machines can barely run basic office software, let alone the resource-intensive AI applications like Tensor Flow or PyTorch. How can students gain hands-on experience with AI software when the hardware itself is inadequate and shared so thinly, creating long queues and limited actual practice time?” Policy: Government and institutional infrastructure development plans consistently fail to prioritise robust digital readiness for TVET institutions in underserved rural and peri-urban areas, leading to an ever-widening gap in access to essential and effective AI learning environments. This neglect compounds the digital divide, making meaningful AI adoption virtually impossible in these regions.
2. High Cost of AI Tools and Data for Marginalised Students	– Exorbitant data costs for accessing online AI platforms and resources. – High financial barrier to acquiring AI-capable personal hardware/software. – Absence of free or subsidised AI learning materials and platforms.	The cost of data bundles in Zimbabwe is prohibitive for the vast majority of the students. They either cannot afford consistent access or quickly deplete their bundles, cutting off their participation in crucial online AI courses, collaborative coding projects, and access to necessary research tools from outside the campus. “There are few national or institutional programs that provide subsidised laptops or even affordable data packages specifically for economically disadvantaged TVET students. The assumption that everyone or majority can afford a capable device and continuous internet access is deeply flawed, especially in high-density areas or peri-urban settlements where income is severely constrained. This effectively makes AI education a privilege, not an accessible opportunity. Policy: There is a glaring absence of targeted government or institutional policies for digital affordability, such as data subsidies, low-cost device schemes, or guaranteed free access to high-quality AI learning platforms. This economic barrier effectively excludes economically disadvantaged students from meaningfully participating in the AI revolution, exacerbating existing socio-economic stratification.
3. Limited Digital Literacy and Awareness of AI’s Vocational Potential	– Lack of foundational digital skills among TVET faculty and students. – Low awareness of AI’s immediate relevance and application to specific trades/vocations. – Inadequate training and support for educators on integrating AI into diverse curricula.	Many TVET instructors, especially those with longer tenure, are not yet proficient with even basic digital tools for teaching, let alone comfortable integrating complex AI concepts into their vocational subjects. How can they effectively train students for an AI-driven future in plumbing, electrical work, or carpentry if they themselves are struggling with digital pedagogy and AI fundamentals? Students often view AI as an abstract concept exclusively for IT specialists or software developers, failing to grasp how it can revolutionise their specific vocational fields, for example, in predictive maintenance for mechanics, smart irrigation in agriculture, or automated design in construction. The current curriculum does not effectively demonstrate practical, real-world AI applications relevant to their everyday vocational practices, leading to disinterest and a perception of irrelevance. Policy: TVET institutions demonstrate a profound lack of comprehensive, ongoing professional development programs for faculty in foundational digital literacy and practical AI integration across all disciplines. Furthermore, national curricula fail to embed core AI literacy and relevant use cases across all vocational disciplines, perpetuating a narrow, IT-centric view of AI’s utility and significantly hindering widespread adoption and understanding among a diverse student body.

From table 6, the themes emerging from Objective 2 starkly illuminate how the digital divide in Zimbabwe acts as a formidable barrier, effectively ghettoizing AI access within TVET. The study found that there is inadequate digital infrastructure, particularly in rural and peri-urban TVET institutions. This extends beyond merely limited internet connectivity to encompass an acute shortage of functional, AI-capable devices and an unreliable power supply. Chisikwa & Shava (2021) also noted that institutional and national policies have often neglected strategic investment in these foundational elements, contributing to a stark geographical disparity. This creates an insurmountable practical barrier, explicitly excluding marginalised students from the immersive and hands-on learning necessary for AI. From the study, the high cost of AI tools and data renders AI adoption a luxury few can afford. Lastly, the limited digital literacy and awareness of AI’s vocational potential among potential or students underscores a profound failure in human capital development and curriculum integration. Therefore, the study noted that without a concerted, institution-wide effort to up skill educators in digital literacy and AI pedagogy, and curricula that explicitly demonstrate AI’s practical relevance across diverse vocational trades, AI remains an esoteric concept.

4        Conclusion and policy implications

The findings from this study, grounded in the realities of many African contexts, reveal a stark and truth for AI adoption in Zimbabwean TVET. The objectives of assessing equity/inclusiveness barriers and investigating the digital divide’s impact are met with overwhelming evidence of systemic failure and deep-seated inequities.

The lack of dedicated funding, severe inadequacy of staff training, minimal curriculum integration, and utter disregard for accessibility for persons with disabilities illustrate a sector fundamentally unprepared, or simply unable, to genuinely embrace AI equitably. The rhetoric of AI transforming TVET is critically undermined by the absence of foundational support and inclusive planning.

The urban-rural divide is not a benign geographical distinction but an active mechanism of exclusion. Rural TVET institutions are systematically denied reliable internet, adequate devices, consistent power, and specialised hardware. This creates a two-tiered system where advanced AI training is a privilege of urban centres, further marginalizing rural populations from participating in the future economy. The regression analysis definitively proves that these are not peripheral issues but central, statistically significant barriers.

Policy implications

In order for AI adoption in Zimbabwean TVET to move beyond aspirational policy documents, a radical shift in investment and strategic planning is imperative. There is need for: 

• Substantial, targeted Funding, earmarked specifically for AI infrastructure, hardware, and sustainable connectivity in all TVET institutions, with particular emphasis on rural areas.
• Comprehensive and continuous training programs for educators, focusing not just on AI tools but also on pedagogical integration and ethical considerations.
• Policy frameworks that legally enforce accessibility features for people with disabilities in all digital learning resources and AI tools.
• A coordinated effort to bridge the rural electrification and internet connectivity gaps, recognising them as fundamental preconditions for any digital transformation.

References

African Development Bank [AfDB]. (2023). African Economic Outlook 2023: Mobilizing Private Sector Financing for Climate and Green Growth in Africa. AfDB.

African Union [AU]. (2022). Africa’s Digital Transformation Strategy (2020-2030). AU.

Buolamwini, J. & Gebru, T. (2018). Gender Shades: Intersectional Phenotypic Biases in Predictive Gender Classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency. In: Proceedings of Machine Learning Research, 81, 77-91.

Chisikwa, D. & Shava, E. (2021). Integrating technology and gender equity in technical and vocational education and training institutions in Zimbabwe: Challenges and opportunities. In: Journal of Technical Education and Training (JTET), 13, 2, 1-15.

GSMA. (2023). The Mobile Economy Sub-Saharan Africa 2023. GSMA.

International Telecommunication Union [ITU]. (2023). Facts and Figures 2023: Statistics on ICT development. ITU.

Ministry of Higher and Tertiary Education, Innovation, Science and Technology Development (Zimbabwe). (2020). Education 5.0 Policy Document. MHTEISTD. Zimbabwe.

Mutambara, J. (2022). Artificial Intelligence and the future of work in Africa: Bridging the digital divide in Zimbabwe’s education sector. In: African Journal of Science, Technology, Innovation and Development, 14, 4, 1017-1027.

Postal and Telecommunications Regulatory Authority of Zimbabwe [POTRAZ]. (2023). Sector Performance Report (Q1 2023). POTRAZ. Zimbabwe.

PricewaterhouseCoopers (PwC). (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC.

UNESCO. (2019). Artificial Intelligence in Education: Compendium of Promising Initiatives. Mobile Learning Week 2019. Online: https://unesdoc.unesco.org/ark:/48223/pf0000370307 (retrieved 11.03.2026).

UNESCO. (2023). Digital transformation in African education: Challenges and opportunities for AI in TVET. UNESCO Regional Office for Southern Africa. UNESCO.

World Bank. (2022). Connecting Africa: The Path to Digital Transformation. World Bank. USA.

World Economic Forum. (2023). The Future of Jobs Report 2023. World Economic Forum. USA.

Zimbabwe National Statistics Agency (ZIMSTAT). (2022). Population and Housing Census 2022: Preliminary Report. ZIMSTAT. Zimbabwe.

Abstract

Artificial intelligence (AI) is transforming teaching and learning in technical and vocational education and training (TVET), yet educators’ readiness for AI-supported instruction remains underexplored in developing contexts. This study assessed the levels and interrelationships of AI readiness, digital literacy, pedagogical adaptability, perception of AI, and attitudinal competencies among TVET educators, as well as differences across selected demographic variables. A cross-sectional survey design was adopted, involving 416 university-based TVET educators. Data were analysed using descriptive statistics, ANOVA, and correlation techniques. Findings revealed moderate levels of AI readiness and digital literacy, alongside high levels of pedagogical adaptability, perception of AI, and attitudinal competencies. Educators demonstrated strong foundational competencies but limited engagement with advanced AI-enabled instructional practices. Significant differences were observed in pedagogical adaptability across age groups, while attitudinal competencies were highest among educators with six to ten years of teaching experience. The competency dimensions were positively and significantly interrelated, indicating a coherent readiness ecosystem. Overall, while TVET educators exhibit favourable dispositions towards AI, their readiness for AI-supported instruction is still emerging, highlighting the need for structured professional development, improved digital infrastructure, and institution-wide AI integration frameworks. 

Keywords: AI readiness, TVET educators, digital literacy, pedagogical adaptability, AI perception, vocational education.

1           Introduction

Artificial intelligence (AI) represents one of the most transformative developments in 21st-century education, reshaping instructional design, assessment, and curriculum delivery through personalization, automation, and data-driven insight. In Technical and Vocational Education and Training (TVET), this shift is particularly critical, aligning educational delivery with competencies required in a rapidly evolving digital economy (Johnson 2022; Do 2025). Adoption of AI extends beyond intelligent tools; it requires rethinking how educators design lessons, support learners, and sustain professional identity within digital environments (Ifeanyi & Okoye 2023; Ibrahim et al. 2025). In developing contexts such as Nigeria, readiness for AI-supported instruction depends on educators’ digital literacy, pedagogical adaptability, and attitudinal dispositions that foster innovation (Ogunode 2022; Oviawe 2020). Globally, AI-driven systems, including adaptive learning platforms, predictive analytics, and intelligent tutoring, are reshaping education by enabling efficient, personalized, and interactive learning (Zawacki‑Richter et al. 2019; Ejiofor & Nwakile 2016). Within TVET, these technologies facilitate simulations, automate assessments, and feedback mechanisms that strengthen practical and cognitive skills essential for employability (Bakar et al. 2024; Do 2019). Yet disparities in preparedness persist, particularly in developing countries where institutional support, infrastructure, and professional capacity remain limited (Mishra et al. 2023; Nwakile et al. 2025). Nigerian educators face the dual challenge of mastering AI applications while adapting teaching practices to meet emerging standards of digital competence (Okwelle & Okoye 2022; Chinedu-Eze et al. 2018), underscoring the need to assess their readiness comprehensively.

Readiness is multidimensional, encompassing digital literacy, pedagogical adaptability, perceptions, and attitudinal competencies (Eze & Nwosu 2021). Digital literacy involves navigating platforms, evaluating information, and applying technology effectively (Ng & Park 2022). Pedagogical adaptability reflects flexibility in adjusting strategies to diverse learner needs and technological innovations (Bates 2021). These competencies are vital in AI-supported environments where teachers must balance machine-assisted learning with human interaction. However, outdated curricula, infrastructural gaps, and limited exposure to AI tools hinder their development (Onyema et al. 2023). Teachers’ perceptions and attitudes also shape adoption, positive views encourage experimentation, while concerns about ethics, privacy, or job security may inhibit use (Dwivedi et al. 2021; Adebayo & Musa 2022). Attitudinal competence, openness, ethical reflection, and professional commitment ensure AI integration remains aligned with human values (Emejulu & Ogbuanya 2020; Usman et al. 2024). 

This study provides a holistic analysis of Nigerian TVET educators’ readiness for AI-supported instruction. Apart from measuring the level of TVET educators’ AI readiness, this study answers the following research questions: 

1. What are the levels of digital literacy, pedagogical adaption, perception of AI and attitudinal competencies among TVET educators?
2. Are there significant differences in these competency dimensions across demographic variables?
3. What relationships exist among AI readiness and the competency dimensions?

By addressing these objectives, the study contributes empirical evidence to inform professional development, curriculum design, and institutional frameworks that support sustainable AI integration in Nigerian TVET institutions. The findings are expected to strengthen educators’ capacity to harness AI for skill development, innovation, and inclusive vocational education.

2            Methodology

2.1       Context, Design, and Participants 

This study was situated within the context of TVET in higher education across Nigeria’s six geopolitical zones, where artificial intelligence (AI) tools such as GitHub Copilot and ChatGPT are adopted to support instruction. The region’s universities are actively exploring AI-supported pedagogies to enhance TVET education. Still, questions remain about the readiness of educators in digital literacy, pedagogical adaptability, perceptions of AI and attitudinal competencies for AI-supported instruction. Based on a correlational survey design, the study specifically targeted TVET educators, who provide authentic opportunities for teachers to encounter both the benefits and challenges associated with AI-assisted learning. Using a convenience sampling technique, we collected 416 valid responses from educators. The TVET educators comprise higher education teachers from Agricultural education 151 (36.3%) lecturers, Business Education 120 (28.8%) lecturers, Computer education 38 (9.1%) lecturers, Home Economics education 36 (8.75), Industrial Technical Education 60 (14.4%) and others 11 (2.6%), including those in communication, GSM repairs, Graphic designs etc. The gender, age, educational qualification, and teaching experience of the respondents are presented in Table 1. 

2.2       Data Collection Instrument

We employed five validated constructs, which were adapted to the context of TVET educators in Nigeria. Each construct was adapted from prior work, refined through expert review, and subjected to reliability and validity checks during pilot testing. All items were measured using a 5-point Likert scale, ranging from 1 = Strongly Disagree to 5 = Strongly Agree, allowing mixed responses rather than the traditional 4-point scale, ensuring sensitivity to gradual changes in perception over time (see full-scale items in the appendix).

The AI Readiness for TVET Educators was adapted from Asmaa’Hussein et al. (2025), with modifications to reflect TVET educators’ context. The construct comprised 6 items, on four key dimensions: perceived usefulness, pedagogical and professional alignment, concerns and risks, and ease of use and confidence. The Digital Literacy Scale was adapted from Zhang and Zhang (2024) and Baskara (2025), who outlined digital literacy for AI in education and society. The construct comprised 12 items. Similarly, the pedagogical adaptability construct with 10 items was adapted from Alqarni (2025) and Dewi et al. (2025), with slight modifications. Furthermore, the perception of AI construct was adapted from Asmaa’Hussein et al. (2025). The construct comprised 12 items, modified to reflect TVET educators’ perceptions of AI. Lastly, a 10-item Attitudinal Competencies construct was adapted from Lāma & Lastovska (2025), with modifications to reflect the TVET educators’ context. 

2.3       Data Collection Procedure & Analysis

Data collection spanned four months of the 2024/2025 academic session (May–August 2025) and followed a structured, ethically approved process ensuring anonymity, voluntary participation, and confidentiality. Approval was obtained from institutional Research Ethics Committees, and participants provided written informed consent. The questionnaire was administered in both print and online via Google Forms, with links distributed via email and professional WhatsApp groups. Before full deployment, the instrument was pilot‑tested with 37 TVET educators, yielding strong internal consistency (Ordinal alpha > 0.80). Responses from 416 educators were analyzed using the Statistical Package for the Social Sciences (SPSS) version 26. Descriptive statistics (mean, standard deviation, frequency, percentage) summarized demographic characteristics and assessed levels of readiness, digital literacy, pedagogical adaptability, AI perception, and attitudinal competencies. The interpretation of mean scores was based on real limits of numbers. Given that Likert-scale items yield discrete numerical responses but are intended to approximate a continuous latent construct, real limits were employed. Each observed scale value was therefore assumed to represent a class interval extending 0.50 units above and below the integer score. Since the instrument was structured on a five-point Likert scale, the mean scores between 0.50-1.49 were interpreted as very low, 1.50–2.49 as low, 2.50–3.49 as moderate, 3.50–4.49 as high, and 4.50–5.49 as very high. Furthermore, one-way analysis of variance (ANOVA) was employed to examine differences in digital literacy, pedagogical adaptability, perceptions of AI, and attitudinal competencies across demographic categories (gender, age, teaching experience, degree and TVET specialization), at 0.05 level of significance. Pearson Product–Moment Correlation (PPMC) was used to determine the relationships among digital literacy, pedagogical adaptability, perceptions of AI, attitudinal competencies, and AI readiness. The strength of the correlations was interpreted based on conventional benchmarks. This multi-stage analytical strategy strengthened methodological rigour and captures the structural complexity of AI readiness without reducing it to a single predictive pathway. 

3        Results 

3.1       Demographic Information of Respondents 

According to the sample statistics in Table 1, the respondents were predominantly male (70%), indicating that, to a large extent, male figures still dominate the TVET skill areas. While most respondents were 34 years and above, slightly more than half (56.5%) had 6-10 years of teaching experience, indicating that securing employment as an educator requires both time and experience that can only be built over the years. Other demographic variables in Table 1 show that 78.4% of the respondents have obtained a PhD degree, which is one major requirement to lecture in Nigerian Universities; and Table 1 also shows that the largest TVET specialization represented among the respondents was from Agricultural Education (36.3%) and Business Education (28.8%).

Table 1: Demographic Information of Respondents 

Demographic Information	Category	Frequency	Percent
Gender	Male	291	70.0
	Female	125	30.0
Age	29 – 33 Years	38	9.1
	34 Years & Above	378	90.9
Educational Qualification	B.Sc/B.Ed	12	2.9
	M.Ed/M.Tech/M.Sc	78	18.8
	PhD	326	78.4
Teaching Experience	1 – 5 Years	42	10.1
	6 – 10 Years	235	56.5
	11 Years & Above	139	33.4
TVET Area	Agricultural Education	151	36.3
	Business Education	120	28.8
	Computer Education	38	9.1
	Home Economics Education	36	8.7
	Technical Education	60	14.4
	Others	11	2.6

3.2       Levels of digital literacy, pedagogical adaptation, perception of AI and attitudinal competencies among TVET educators

3.2.1       TVET Educators’ Level of AI Readiness

The educator’s AI readiness items in Table 2 ranged from 3.09 to 3.53. Based on the real limits, only item 1, “I understand how to use AI tools relevant to my vocational teaching“, marginally entered the high category (M = 3.53); most of the items fall within 2.50 – 3.49, indicating a moderate level of AI readiness among the TVET educators. Moreover, the cluster mean of 2.50 falls exactly at the lower boundary of the moderate level, thus confirming that TVET educators demonstrated a moderate level of readiness for AI-supported instruction, reflecting emerging but not yet robust preparedness. 

Table 2: TVET Educators’ Level of AI Readiness 

S/N	Items	Mean	Std. Deviation
1	I understand how to use AI tools relevant to my vocational teaching.	3.53	1.04
2	I can integrate AI-powered assessment tools (e.g., auto-grading, feedback) into my lessons.	3.44	1.03
3	I am confident that AI can personalize learning experiences for my students.	3.30	1.11
4	I am concerned that AI may introduce bias or unfairness in student evaluation	3.22	1.16
5	I know how to protect student data when using AI platforms.	3.40	1.20
6	I am willing to explore new AI tools to enhance vocational training delivery	3.09	1.21
	Cluster Mean	2.50	0.53

3.2.2      TVET Educators’ Level of Digital Literacy 

Table 3 shows the overall cluster mean of 3.43 (SD = 0.63), indicating a moderate level of digital literacy among TVET educators. Although some competencies were observed to be high, such as cybersecurity practices (M = 4.00), software installation (M = 3.85) and ethical use of digital resources (M = 3.82), some were also low, including integrating simulations or virtual labs into lessons (M = 2.27) and using digital assessment tools (M = 2.31). This outcome simply implies that TVET educators are less proficient in advanced technology-integrated instructional practices. 

Table 3: Digital Literacy Level of TVET Educators 

S/N	Items	Mean	Std. Deviation
1	I can install and update software relevant to my teaching.	3.85	1.23
2	I can use cloud storage to organize and share teaching materials.	3.39	1.05
3	I can identify credible online sources for vocational training.	3.72	0.97
4	I can analyze digital data (e.g., spreadsheets, dashboards) to inform teaching.	3.45	1.06
5	I integrate simulations or virtual labs into my lessons.	2.27	1.04
6	I use digital assessment tools (e.g., quizzes, e-portfolios) to evaluate student learning.	2.31	1.18
7	I use digital platforms to maintain communication with industry stakeholders.	3.09	1.21
8	I encourage students to collaborate using online tools (e.g., Google Workspace, MS Teams).	3.80	1.00
9	I adapt open educational resources (OER) for my teaching context.	3.15	1.04
10	I design multimedia presentations that enhance practical demonstrations.	3.12	1.11
11	I model ethical use of digital resources (avoiding plagiarism, respecting copyright).	3.82	1.06
12	I implement cybersecurity practices (e.g., strong passwords, secure storage).	4.00	0.94
	Cluster Mean	3.43	0.63

3.2.3       TVET Educators’ Level of Pedagogical Adaptability 

Table 4 shows that the mean values ranged from 3.83 to 4.21, indicating a near-consistent high level of pedagogical adaptability among the TVET educators. The cluster mean of 4.02 clearly indicates a high level of pedagogical adaptability among TVET educators. This suggests strong flexibility in instructional strategies, learner support, industry integration and reflective practice. 

Table 4: Pedagogical Adaptability Level of TVET Educators

S/N	Items	Mean	Std. Deviation
1	I can switch between demonstration, discussion, and hands-on practice depending on learner needs.	3.98	0.97
2	I adjust lesson pacing when students require more time to grasp concepts.	3.85	1.03
3	I adapt my teaching to accommodate students with different learning styles.	4.16	0.86
4	I provide additional support for learners who are struggling without slowing down the whole class	4.01	0.92
5	I integrate real-world case studies from industry into my lessons.	4.00	0.79
6	I update my teaching content when new technologies or processes emerge in the workplace.	4.21	0.84
7	I use both practical and theoretical assessments to evaluate student competence.	4.06	1.01
8	I adapt assessment methods for learners with special needs or different learning contexts.	3.83	0.76
9	I reflect on my teaching after each lesson and make adjustments for improvement.	4.09	0.81
10	I try out new teaching strategies even if they may not work perfectly at first.	4.05	0.75
	Cluster Mean	4.02	0.58

3.2.4       TVET Educators’ Perception of AI Level

In Table 5, the cluster mean (M = 3.81) indicates that TVET educators’ overall perception of AI is high, reflecting strong recognition of AI’s instructional value, relevance to competency development and potential for enhancing vocational training. 

Table 5: TVET Educators’ Perception of AI Level 

S/N	Items	Mean	Std. Deviation
1	AI can help personalize learning for students with different abilities.	4.07	0.96
2	AI can reduce my administrative workload (e.g., grading, scheduling).	4.15	0.74
3	AI can enhance student engagement in vocational training.	4.12	0.82
4	I understand how to use AI tools relevant to my teaching.	3.70	0.96
5	I would need significant training before I could use AI effectively. (reverse-coded)	3.55	1.21
6	I feel comfortable experimenting with AI-powered teaching tools.	3.65	1.05
7	AI may introduce bias or unfairness in student assessment.	3.47	0.88
8	I worry that students may become overly dependent on AI tools.	4.02	1.04
9	AI could threaten the professional role of educators in the future.	3.42	1.18
10	AI can simulate real-world industry scenarios for training purposes.	3.88	0.91
11	AI can complement, but not replace, hands-on practical training.	3.93	1.00
12	AI aligns with the competency-based approach of TVET.	3.72	0.89
	Cluster Mean	3.81	0.49

3.2.5      TVET Educators’ Level of Attitudinal Competency

Data in Table 6 show that all the items, including the cluster mean, had mean values above 4.00, indicating that TVET educators have a high level of attitudinal competence. This result reflects strong professional values, openness to innovation, industry collaboration, reflective practice, and a positive disposition towards teaching and mentorship. 

Table 6: TVET Educators’ Attitudinal Competency Level 

S/N	Items	Mean	Std. Deviation
1	I respect the diverse cultural and social backgrounds of my students.	4.01	0.94
2	I am patient when students struggle to master practical skills.	4.04	0.77
3	I continuously seek opportunities to improve my teaching practice.	4.18	1.01
4	I take pride in being a role model for professional conduct in my field.	4.20	0.83
5	I am open to experimenting with new teaching strategies.	4.20	0.84
6	I remain positive when faced with unexpected challenges in the classroom.	4.07	0.80
7	I value collaboration with industry partners to enrich student learning.	4.36	0.69
8	I willingly mentor less experienced colleagues.	4.07	0.77
9	I treat all students fairly, regardless of their performance level.	4.38	0.78
10	I reflect on my teaching to identify areas for improvement.	4.24	0.75
	Cluster Mean	4.18	0.58

3.3       Competency Dimensions across Demographic Variables

3.3.1      Digital Literacy and Demographic Variables of TVET Educators 

Data in Table 7 show that, except for TVET specialization, none of the demographic variables showed a statistically significant effect on digital literacy levels. This implies that digital literacy among TVET educators is relatively consistent across age, gender, qualification, and experience, but varies by subject area, reflecting differences in technology exposure and instructional demands. Thus, the F-value (5.167) with a p-value of 0.000 < 0.05 indicates a statistically significant difference in digital literacy across TVET disciplines. Tukey HSD post-hoc comparison shows that TVET educators in Computer education and those in “others” scored significantly higher than educators in Home Economics and Business Education. This result indicates that disciplinary specialization has significant effects on digital literacy, likely due to differences in exposure to ICT-based teaching environments.

Table 7: ANOVA Results of Digital Literacy and Demographic Variables

Variable	Source	Sum of Squares	df	Mean Square	F/t	Sig.
Gender	Equal variances assumed		414		1.438	0.151
Age	Between Groups	0.061	1	0.061	0.155	0.694
	Within Groups	164.125	414	0.396
	Total	164.187	415
Teaching Experience	Between Groups	0.449	2	0.224	0.566	0.568
	Within Groups	163.738	413	0.396
	Total	164.187	415
Degree	Between Groups	0.193	2	0.096	0.242	0.785
	Within Groups	163.994	413	0.397
	Total	164.187	415
TVET Specialization	Between Groups	9.733	5	1.947	5.167	0.000
	Within Groups	154.454	410	0.377
	Total	164.187	415

3.3.2      Pedagogical Adaptation and Age of TVET Educators 

After mean comparisons across demographic variables and pedagogical adaptations, Table 8 shows the ANOVA result (F= 8.962, p < 01), indicating a significant difference in pedagogical adaptations according to age groups. TVET educators aged 34 years and above (M = 4.05), demonstrated higher pedagogical adaptability than those aged 29 – 33 years (M = 3.76). This suggests that older educators are more capable of integrating new pedagogical strategies, likely because of their teaching experiences. 

Table 8: Descriptives & ANOVA of Pedagogical Adaptation and Age of TVET Educators 

Age	No.	Mean	Standard Dev.	Source	Sum of Squares	df	Mean Square	F	Sig.
29 – 33 Years	38	3.76	.58	Between Groups	.992	2	2.994	8.962	.003
34 Years & Above	378	4.05	.58	Within Groups	140.321	413	.334
Total	416	4.03	.58	Total	141.313	415

3.3.3      Perception of AI and Years of Experience of TVET Educators 

Among the variables, Table 9 shows that only years of experience had a near-significant difference, yet not significantly different (F = 2.914, p > 0.05), in TVET educators’ perception of AI across different years of teaching experience categories. Table 9 also shows a marginal difference in the mean values of the teaching experience of the educators, with those in the 6-10 years of experience (M = 3.86) category appearing slightly more positive towards AI than other groups. However, since the difference is not significant, it implies that TVET educators across the experience levels have generally consistent positive perceptions of AI. 

Table 9: Descriptives & ANOVA of the Perception of AI and Years of Experience among TVET Educators 

Years of Experience	No.	Mean	Standard Dev.	Source	Sum of Squares	df	Mean Square	F	Sig.
1 – 5 Years	42	3.7183	.51236	Between Groups	1.409	2	.705	2.914	.055
6 – 10 Years	235	3.8574	.45703	Within Groups	99.883	413	.242
11 Years & Above	139	3.7482	.54001	Total	101.292	415
Total	416	3.8069	.49404

3.3.4      Attitudinal Competencies and Years of Experience of TVET Educators

The ANOVA result in Table 10 reveals a significant difference (F = 3.630, p = 0.027 < 0.05) in attitudinal competencies across teaching experience levels. TVET educators with 6-10 years of experience (M = 4.24) scored highest, indicating more favourable attitudes towards AI-supported instructions. This is interesting, as the group (6-10 years) likely represents educators in their professional prime, who are technologically confident, yet sufficiently experienced to appreciate pedagogical and ethical implications of AI tools. In contrast, early-career educators (1-5 years) may still be developing these attitudes, while senior educators (11 years and above) may experience inertia in adaptation. 

Table 10: Descriptives & ANOVA of the Attitudinal Competencies and Years of Experience among TVET Educators 

	No.	Mean	Standard Dev.	Source	Sum of Squares	Df	Mean Square	F	Sig.
1 – 5 Years	42	4.0143	.52941	Between Groups	2.385	2	1.193	3.630	.027
6 – 10 Years	235	4.2370	.52537	Within Groups	135.695	413	.329
11 Years & Above	139	4.1216	.65726	Total	138.080	415
Total	416	4.1760	.57682

3.4       Relationship Between Digital Literacy, Pedagogical Adaptation, Perception of AI, Attitudinal Competencies, and AI Readiness

Table 11 shows that digital literacy has a significant positive relationship with pedagogical adaptation (r = .388, p < .01), perception of AI (r = .337, p < .01) and attitudinal competences (r = .284, p = < .01). This implies that digitally literate educators are also more adaptable pedagogically, and have more positive perceptions and attitudes towards AI-supported instructions. Data in Table 11 also show that attitudinal competences strongly correlated with pedagogical adaptation (r = .751, p = < .01) and perception of AI (r = .649, p = < .01), indicating a consistent cluster of affective and instructional readiness dimensions. Readiness to support AI correlated significantly (but weakly) with attitudinal competences (r = .131, p = < .05), suggesting that those with stronger attitudinal orientations tend to express readiness in slightly higher dimensions. 

Table 11: Correlation Matrix of Readiness to Support AI among TVET Educators 

S/N		1	2	3	4	5
1	Digital Literacy	1
2	Pedagogical Adaptation	.388**	1
3	Perception of AI	.337**	.644**	1
4	Attitudinal Competencies	.284**	.751**	.649**	1
5	Readiness_Index	.013	.088	.060	.110*	1

4        Discussion of Findings

The major issue in this study is to examine the readiness of TVET educators for AI-supported instruction across Nigeria’s six geopolitical zones, where artificial intelligence (AI) tools such as GitHub Copilot, ChatGPT, Google Gemini, among others, are adopted to support the instructional process. This study specifically examines the levels of AI readiness, digital literacy, pedagogical adaptation, perception of AI and attitudinal competencies among TVET educators. This study also contributes to the competency dimensions across demographic variables and relationships among AI readiness and the competency dimensions of TVET educators. 

4.1       Demographic Information of Respondents

The demographic analysis revealed a predominantly male workforce (70%), reflecting the gender imbalance historically associated with technical fields. This aligns with Oviawe (2020) and Okwelle and Okoye (2022), who attribute such disparities to societal perceptions that discourage female participation. Nonetheless, the 30% female representation signals gradual inclusivity, consistent with national gender equity policies (Adebayo & Musa 2022). The age distribution showed that most respondents were 34 years and above, suggesting maturity and accumulated teaching or industry experience, corroborating the findings of Usman et al. (2024). Academic qualifications were notably high, with 78.4% holding PhDs, underscoring professional credibility and potential confidence in adopting advanced technologies (Ng & Park 2022). Teaching experience was concentrated among mid-to-late-career educators, a group often receptive to pedagogical innovation (Emejulu & Ogbuanya 2020). Specialization patterns reflected national enrolment trends, with Agricultural and Business Education dominant (Bakar, Ghafar & Abdullah 2024). Collectively, these demographics establish a mature, highly qualified, and experienced educator population, providing a robust foundation for interpreting readiness for AI integration (Sholikhah et al. 2025)

4.2       Levels of digital literacy, pedagogical adaptation, perception of AI and attitudinal competencies among TVET educators

The study examined four critical dimensions of technology integration and instructional readiness among TVET educators: digital literacy, pedagogical adaptation, perception of AI, and attitudinal competencies. Together, these dimensions provide a holistic understanding of how educators navigate the challenges and opportunities of technology-mediated TVET education. Table 2 revealed that TVET educators demonstrated a moderate level of AI readiness. Although educators showed relatively strong understanding of AI tools relevant to vocational teaching and expressed willingness to explore new AI applications, their confidence in integrating AI-powered assessment tools, managing ethical risks such as bias, and protecting student data remained cautious. This pattern suggests that AI readiness among TVET educators is emergent rather than consolidated, reflecting openness and awareness without full operational confidence. This aligns with the findings of Ifeanyi and Okoye (2023) who reported that in developing contexts, educators demonstrate interest in AI-supported instruction but lack structured institutional support and sustained professional development to translate interest into practice. The moderate readiness level also reflects concerns surrounding fairness, data privacy, and professional role integrity, which Dwivedi et al. (2021) and Adebayo and Musa (2022) identified as common moderating factors in AI adoption among educators.

Table 3 revealed a moderate level of digital literacy among TVET educators. This finding suggests that while educators possess foundational digital skills, their proficiency in advanced technology-integrated practices remains limited. High mean scores were observed in cybersecurity practices, software installation, and ethical use of digital resources, reflecting strong awareness of responsible digital behaviour. However, competencies such as integrating simulations or virtual labs and using digital assessment tools were notably low, implying limited adoption of innovative instructional technologies. These results align with Ogunode (2022) and Oviawe (2020), who reported that Nigerian educators often struggle with the practical application of digital tools due to infrastructural gaps and insufficient training. Similarly, Tondeur et al. (2020) emphasized that digital competence requires continuous institutional support and hands-on experience. Within the framework of the Technology Acceptance Model (Davis 1989), perceived ease of use strongly influences adoption; thus, educators with limited exposure to complex technologies may perceive them as difficult to use. Interestingly, demographic variables such as gender, age, qualification, and teaching experience did not significantly influence digital literacy, except for specialization. This suggests that digital literacy is fairly evenly distributed among educators, though those in computing-related disciplines demonstrate higher competence due to greater exposure to technology-based learning environments, corroborating Zawacki-Richter et al. (2019).

Table 4 indicates a consistently high level of pedagogical adaptability among TVET educators. This reflects strong flexibility in instructional strategies, learner support, industry integration, and reflective practice. Notably, adaptability differed significantly by age, with older educators demonstrating greater flexibility in modifying instructional approaches.

This finding supports Dwivedi et al. (2021), who argued that adaptability grows through reflective practice and accumulated teaching experience. Usman et al. (2024) similarly observed that professional development enhances alignment between pedagogy and technology, while Eze and Nwosu (2021) reported higher adaptability among experienced Nigerian educators. Pedagogical adaptability is particularly critical in AI-supported instruction, where educators must continuously adjust to evolving learner needs and technological innovations. Within TAM, perceived usefulness of technology strongly influences adoption, and adaptability reflects educators’ recognition of AI’s instructional value. Thus, age and experience appear to strengthen confidence and flexibility in technology-mediated environments.

Table 5 revealed that TVET educators hold generally positive perceptions of AI. This reflects strong recognition of AI’s instructional value, relevance to competency development, and potential for enhancing vocational training. Although differences across teaching experience were not statistically significant, mid-career educators (6–10 years) expressed slightly higher positivity, suggesting that this group combines competence with curiosity for innovation.

These findings align with Dwivedi et al. (2021) and Nguyen et al. (2023), who noted that perceptions are often shaped by awareness rather than practical engagement. Adebayo and Musa (2022) similarly observed that enthusiasm for technology adoption peaks among mid-career educators. Within TAM, perceived usefulness is a critical determinant of acceptance, and positive perceptions are essential for fostering adoption. However, limited practical exposure suggests that institutional training is necessary to transform favorable perceptions into functional readiness.

Table 6 showed a high level of attitudinal competence among TVET educators. This reflects strong professional values, openness to innovation, industry collaboration, reflective practice, and a positive disposition toward teaching and mentorship. Attitudinal competencies varied significantly across teaching experience, with mid-career educators scoring highest. This suggests that professional maturity fosters openness, confidence, and motivation to integrate AI into pedagogy. The findings correspond with Emejulu and Ogbuanya (2020), who emphasized that attitudes are shaped by exposure and confidence, and with Kim and Kim (2022), who highlighted the role of professional experience in shaping favorable dispositions. Attitudinal competence is critical because it combines ethical awareness, openness to innovation, and motivation to use emerging technologies responsibly (Eze & Nwosu 2021). Within TAM, this dimension mirrors perceived usefulness, as educators who view AI as beneficial are more likely to maintain positive attitudes and integrate it into practice. Thus, attitudinal strength among mid-career educators positions them as potential champions of AI adoption. Summarily, the findings highlight a nuanced profile of TVET educators. While digital literacy remains moderate, pedagogical adaptability, perception of AI, and attitudinal competencies are consistently high. This suggests that educators are flexible, positive, and ethically grounded, but require targeted training and infrastructural support to strengthen advanced digital practices. The Technology Acceptance Model provides a useful lens, showing that perceived ease of use and perceived usefulness shape adoption. Therefore, institutional interventions that simplify technology use and demonstrate its instructional value can accelerate integration. Mid-career educators, with their balance of experience and openness, emerge as key drivers of AI-supported instruction, capable of championing innovation in vocational education.

Hence, the findings on digital literacy, pedagogical adaptability, perception of AI, and attitudinal competencies provide a coherent explanation for the observed level of AI readiness among TVET educators. While digital literacy remains moderate and constrains engagement with advanced AI-enabled instructional practices, high pedagogical adaptability, positive perceptions of AI, and strong attitudinal competencies create a favourable foundation for readiness. These dimensions are mutually reinforcing rather than independent; pedagogical flexibility enables experimentation, positive perceptions motivate engagement, and attitudinal competence sustains ethical and reflective use of AI. Consequently, the moderate level of AI readiness observed in this study reflects a transitional stage, where foundational competencies and favourable dispositions exist, but deeper technical proficiency and institutional scaffolding are required for full readiness. 

4.3       Competency Dimensions Across Demographic Variables

The study revealed selective influences of demographic variables on TVET educators’ competencies, suggesting that readiness-related competencies are shaped more by contextual exposure than by demographic characteristics alone. Digital literacy was generally consistent across age, gender, qualification, and experience, but varied significantly by specialization. Educators in Computer Education and related fields scored higher than those in Home Economics and Business Education, reflecting greater exposure to ICT-based instructional environments (Zawacki-Richter et al. 2019). This supports earlier findings that moderate digital literacy persists due to infrastructural gaps and limited access to advanced training opportunities (Ogunode 2022; Oviawe 2020; Tondeur et al. 2020). Pedagogical adaptability differed significantly by age, with older educators showing greater flexibility, indicating that instructional adaptability strengthens through accumulated teaching experience and reflective practice (Dwivedi et al. 2021; Eze & Nwosu 2021). Perceptions of AI were consistently positive across experience levels, though mid-career educators expressed slightly higher enthusiasm (Nguyen et al. 2023; Adebayo & Musa 2022). Attitudinal competencies varied significantly, with mid-career educators scoring highest (M = 4.24), reflecting confidence, openness, and ethical awareness associated with professional maturity (Emejulu & Ogbuanya 2020; Kim & Kim 2022).

4.4       Relationships between AI Readiness and the Competency Dimensions

Table 11 highlights significant positive relationships among digital literacy, pedagogical adaptation, perception of AI, and attitudinal competencies. Digital literacy correlated with pedagogical adaptation, perception of AI, and attitudinal competence, suggesting that digitally skilled educators are more adaptable and hold favorable perceptions and attitudes toward AI-supported instruction. Attitudinal competence showed strong correlations with pedagogical adaptation and perception of AI, underscoring the interconnectedness of affective and instructional readiness. This indicates that openness, ethical awareness, and professional commitment reinforce educators’ capacity to adapt pedagogically and engage constructively with AI-enabled tools. Readiness to support AI correlated weakly but significantly with attitudinal competence, indicating that positive professional orientations modestly enhance readiness, even when advanced technical skills are still developing. These results support Zawacki-Richter et al. (2019), who emphasized the synergy between technology and pedagogy, and Usman et al. (2024), who noted that emotional and pedagogical orientations improve instructional outcomes. Similar conclusions were reached by Dwivedi et al. (2021) and Adebayo and Musa (2022), who argued that educators’ psychological readiness often precedes full technological adoption. Overall, readiness appears more dependent on psychological and instructional dimensions than demographic factors.

5        Conclusion and Recommendations

This study examined TVET educators’ readiness for AI-supported instruction in Nigerian higher education, focusing on digital literacy, pedagogical adaptability, AI perception, attitudinal competencies, and demographic influences. Educators displayed moderate digital literacy, with stronger basic skills but weaker advanced AI applications such as simulations, analytics, and virtual labs. Older educators showed greater pedagogical adaptability, while mid-career educators demonstrated stronger attitudinal competencies, reflecting openness and confidence. Although skill-based variables were positively interrelated, overall readiness for AI-supported instruction remained moderate, suggesting that preparedness is still emerging within the TVET context. Collectively, educators are receptive yet under-prepared, constrained by structural enablers rather than willingness. Universities and TVET faculties should implement sustained, discipline-specific AI capacity-building programs emphasizing instructional applications, learning analytics, simulations, and AI-assisted assessment design, alongside improvements in institutional infrastructure and support systems.

6        Limitations and Suggestions for Further Studies

This study relied on self-reported data, which may introduce bias and not fully capture classroom practices or AI proficiency. Its cross-sectional design offered only a snapshot, while the regression explained limited variance, leaving institutional factors unmeasured. Nonetheless, diverse regional sampling strengthens contextual validity. Future research should employ longitudinal or experimental designs, integrate institutional variables and direct usage measures, and pursue comparative studies across systems to deepen understanding and inform scalable AI integration in TVET education.

References

Adebayo, S. & Musa, A. (2022). ‘Educators’ attitudes toward artificial intelligence integration in Nigeria’s higher education’. In: African Journal of Educational Technology, 9, 2, 112-128.

Asmaa’Hussein, N., Chin, M.K.S. & Zainuddin, K. (2025). ‘Exploring the relationship between students’ perceptions, self efficacy, and challenges in using artificial intelligence tools in TVET learning’. In: Borneo Engineering and Advanced Multidisciplinary International Journal, 4(Special Issue), 72-78.

Bakar, M.H.A., Ghafar, N.H.M. & Abdullah, F. (2024). ‘Exploring the integration of artificial intelligence in technical and vocational education and training’. In: Borneo Engineering and Advanced Multidisciplinary International Journal, 3, 3, 57-63.

Bates, T. (2021). Teaching in a digital age: Guidelines for designing teaching and learning. Vancouver: Tony Bates Associates.

Chinedu-Eze, S., Chinedu-Eze, V.C. & Bello, A.O. (2018). ‘Utilisation of e learning facilities in the educational delivery system of Nigeria’. In: International Journal of Educational Technology in Higher Education. 15, 34. Online: https://doi.org/10.1186/s41239-018-0116-z (retrieved 05.03.2026).

Dewi, D. S., Siahaan, S., Wilany, E., Adam, A., Ramadhani, R., Saicindo, S. A., & Ardhi, M. A. (2025). ‘Enhancing adaptability in educational research: the role of AI driven tools’. In: Panicgogy International Journal, 3, 1, 10-17.

Do, C. L. T. (2019). Integrating Essential Skills into Training Programs at Ho Chi Minh City Vocational College: Implementation Process and Results. In: TVET@Asia, issue 12, 1-20. Online: http://www.tvet-online.asia/issue12/Do_tvet12.pdf (retrieved 05.03.2026).

Do, C.L.T., (2025). Social-emotional development for vocational school students through teaching soft skills: A case study in Vietnam. In: TVET@Asia, issue 25, 1-15. Online: https://tvet-online.asia/wp-content/uploads/2025/08/TVET@Asia_Issue-25_paper-5-5.pdf (retrieved 05.03.2026).

Dwivedi, Y.K. et al. (2021). ‘Artificial intelligence in higher education: Challenges and opportunities’. In: International Journal of Information Management, 57, 102400.

Ejiofor, T.E. & Nwakile, T.C. (2016). ‘Revitalising technical vocational education and training for job creation’. In: Journal of Association of Vocational and Technical Educators of Nigeria, 21, 2, 102-110. Online: https://avten-edu.org.ng/journal/index.php/javten/article/view/168 (retrieved 05.03.2026).

Emejulu, K.C. & Ogbuanya, T.C. (2020). ‘Teachers’ professional adaptation to digital transformation’. In: Nigerian Journal of Technical Education, 15, 2, 95-110.

Eze, F.C. & Nwosu, E.I. (2021). ‘Digital readiness and pedagogical competence’. In: Journal of Educational Technology and Development Studies, 4, 1, 15-29.

Ibrahim, R., Ariffin, K.M., Rejab, M.M., Jamel, S. & Razzaq A.R.A. (2025). Career Advancement for Students Undertaking TVET Matriculation Program after the High School in Malaysia. In: TVET@Asia, issue 25, 1-14. Online: https://tvet-online.asia/startseite/career-advancement-forstudents-undertaking-tvet-matriculation-program-after-high-school-in-malaysia/ (retrieved 06.08.2025).

Ifeanyi, C. & Okoye, P. (2023). ‘Assessing teachers’ preparedness for artificial intelligence in Nigerian education’. In: International Journal of Educational Studies in Technology and Information, 5, 1, 51-63.

Izuakor, C.F. & Eze, C.U. (2024). ‘Institutional support and technology adoption behaviour in TVET’. In: Journal of Vocational and Technical Teacher Education, 14, 1, 72-89.

Johnson, M. (2022). ‘Artificial intelligence and the future of vocational education’. In: Educational Futures Review, 8, 3, 145-161.

Kim, N. J &Kim, M.K. (2022). ‘Teachers’ perceptions of using an artificial intelligence based educational tool for scientific writing’. In: Frontiers in Education, 7, 755914. Online: https://doi.org/10.3389/feduc.2022.755914 (retrieved 05.03.2026).

Lāma, G. & Lastovska, A. (2025). ‘AI competence and sentiment: A mixed methods study’. In: Frontiers in Artificial Intelligence, 8, 1658791.

Mishra, P., Koehler, M.J. & Cain, W. (2023). ‘Impact of technological knowledge on innovative teaching’. In: Educational Technology Research and Development, 71, 1, 45-62.

Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. In: Educational Technology Research and Development, 71, 1, 137-161. Online: https://doi.org/10.1007/s11423-023-10203-6 (retrieved 05.03.2026).

Nguyen, N.T., Pham, H.L. & Tran, M.Q. (2023). ‘Teachers’ attitudes toward AI in education’. In: Education and Information Technologies, 28, 2, 1475-1493.

Nwakile, T. C., Ugwuoke, C. U., Okadi, A., Ali, C. C., Nnadi, H. S., Ikehi, M., Ekwueme, S. U., Okanya, V. A., Eya, C. C., & Emeka, O. (2025). Retrospective assessment of academic and psychological readiness of final-year technical and vocational education students in Nigeria after the 2022 ASUU strike. In: Discover Education, 4, Article 409. Online: https://doi.org/10.1007/s44217-025-00893-w (retrieved 05.03.2026).

Ogunode, N.J. (2022). ‘Barriers to technology integration’. In: Journal of Educational Policy and Innovation, 6, 1, 54-68.

Okwelle, P.C. & Okoye, I.R. (2022). ‘Infrastructural and capacity challenges in TVET’. In: African Journal of Technical Education and Innovation, 12, 2, 61-78.

Onyema, E.M., Nwankwo, C.A. & Ugwu, F.O. (2023). ‘Digital transformation and adaptive pedagogy’. In: Education and Information Technologies, 28, 4, 5983-6002.

Oviawe, J.I. (2020). ‘Technology integration in TVET in Nigeria’. In: Journal of Technical Education Research and Development, 3, 1, 25-39.

Sholikhah, S.S., Zamzami, N. & Sulistyaningsih (2025). ‘Teachers’ and students’ perceptions of AI’. In: Journal of Scientific Research, Education and Technology (JSRET), 4, 3, 1474-1482. Online: https://doi.org/10.58526/jsret.v4i3.318(retrieved 05.03.2026).

Usman, A.S., Ali, M.Y. & Sule, B. (2024). ‘Teachers’ attitudes, adaptability and digital competence’. In: International Journal of Educational Innovation, 11, 2, 211-230.

Zawacki-Richter, O. et al. (2019). ‘Systematic review of AI applications in higher education’. In: International Journal of Educational Technology in Higher Education, 16, 39.

Zhang, J. & Zhang, Z. (2024). ‘AI in teacher education’. In: Journal of Computer Assisted Learning, 40, 4, 1871-1885.

Abstract

The rapid emergence of artificial intelligence (AI) is transforming global industries and labour markets at an unprecedented rate, making it essential to fundamentally shift technical and vocational education and training (TVET) to equip the future-proof workforce with the essential AI competencies it needs. This shift has significant implications for TVET educators, who must be competent in integrating AI into their teaching and learning processes. This chapter draws on data from an international study examining the current landscape and future trends of adult educators’ practice and perceptions on AI. Whilst the study was conducted across multiple countries, this chapter focuses on findings from Brunei, situating it within broader discourses on AI in adult education. An online survey was conducted between June and August 2024. A total of 118 respondents working across the higher education and TVET institutions completed the survey in Brunei. This survey was used to examine the current landscape and future trends in adult educators’ practice and perceptions of AI in higher education and adult education contexts. Based on the findings, the chapter proposes a multi-layered strategy to empower adult educators, including TVET educators, that embeds AI literacy into their professional learning within a broader digital competence framework. This strategy repositions TVET educators as critical practitioners capable of mediating between AI and pedagogy to ensure AI adoption strengthens, rather than undermines, equity and learner agency. The chapter concludes by advocating for systemic professional learning, communities of practice, and policies that recognise AI competence as central to TVET educator professionalism. 

Keywords: Artificial Intelligence, Adult Educators, Professional Learning framework, Vocational and Technical Education, Workplace Learning

1        Introduction

The exponential growth of artificial intelligence (AI) and broader digitalisation has transformed global industries and labour markets at an unprecedented rate. This transformation, particularly through advancements in AI technology, has profoundly impacted the occupational landscape and the required skill sets for various professions worldwide (Goh 2023a). For instance, workplace AI applications have shifted the focus from routine to non-routine tasks, with workers now spending less time on repetitive outputs and more time using their expertise to assess work quality. This has prompted a significant re-evaluation of educational paradigms, especially in higher education and Technical and Vocational Education and Training (TVET) institutions, to better align with evolving workforce demands. How, then, is AI changing the TVET sector? The transformative trends with the integration of AI in TVET have provided a glimpse of the opportunities made possible through its technologies to improve learning outcomes. These trends in AI are clearly influencing the evolving practices and roles of adult educators. They provoke mixed perceptions about AI’s use in work and learning environments among educators. Some embrace its potential, citing improved pedagogical approaches and more personalised learning experiences via adaptive AI learning systems. However, others voice concerns about ethical dilemmas, digital inequality, and the urgent need for upskilling to implement AI-supported learning in TVET. Much of the literature, including popular, political, and professional discourse, emphasises the wide-ranging opportunities AI provides for educational practices. However, what remains lacking—and is crucial for effective AI integration into TVET—is a focus on adult educators’ readiness to adopt these tools in their work. That is, their understanding of what AI actually entails, its potential applications in their specific vocational domains, and the pedagogical shifts required to leverage AI effectively within the curricula (Petridou & Lao 2024). This tension between potential and readiness shows that technological adoption cannot be reduced to mere access or training; it involves deeper issues of understanding, available opportunities, and infrastructure support (Goh in press). Therefore, we aim to investigate, more broadly, how AI is implemented within Brunei’s training and adult education (TAE) sector by examining the factors influencing AI adoption using Davis et al.’s (1989) Technology Acceptance Model (TAM), such as adult educators’ perceptions, including perceived usefulness. Consequently, this paper also analyses the current AI practices of adult educators, ethical concerns and their professional development activities related to AI adoption in the workplace. These insights are a crucial first step toward developing lifelong professional learning strategies that address pedagogical needs and equip educators with necessary AI literacy. Given this context, the key questions driving this paper are: What are the current practices and perceptions of adult educators using AI? Based on these findings, how can adult educators advance their AI competency through workplace learning?

The article progresses with a review of existing literature on AI in TAE, as well as TVET, and examines several competence and AI literacy frameworks relevant to adult educators’ professional learning. The following section provides an overview of the Technology Acceptance Model (TAM) proposed by Davis et al. (1989), setting the context for the study. We then discuss the current position of AI in Brunei’s education system, highlighting digital transformation progress and the emerging integration of AI. The methodology section outlines our study design, including instrument development and the survey approach used to gather insights into adult educators’ perceptions and practices regarding AI use. Finally, we present our findings with a discussion, which provides the rationale for our proposed multi-layered framework, expanding upon the Digital Empowerment for Lifelong Learning and Transformative Andragogy (DELTA) and AI literacy frameworks, as a roadmap for the successful integration of AI into TVET.

2        AI in training and adult education

The future of AI in higher and adult education, like any aspect of the future, is uncertain, unpredictable, and fundamentally unknowable. On one hand, AI has the potential to transform educational experiences by expanding learning opportunities and changing how knowledge is accessed, assessed, and applied. On the other hand, it introduces challenges and raises ethical considerations when integrated into education. The report by McKinsey Global Institute (2025) suggests that by 2030, significant transitions are expected to accompany the adoption of automation and AI. These changes are already altering occupational profiles and skill demands, as AI seems to be creating roles with entirely new tasks that require innovative skill sets. This points to the need for higher education, including TVET, to develop more flexible, industry-aligned, and technology-driven programmes to address the needs of those who are currently employed and those (re)entering the labour market (Attwell et al. 2021; Goh & Paryono 2024). Such changes have spurred up discourses amongst higher education providers on the relevance of the existing curriculum to keep pace with the evolving skills requirements in the workplace. Increasingly, more emphasis is now placed on developing higher-order cognitive skills, curriculum redesign which incorporates AI-enabled tools for personalised learning, adaptive assessment and real-time feedback (Petridou & Lao 2024). However, the integration of AI requires educators themselves to possess a robust understanding of AI, its applications, and its pedagogical implications. Equally important is for the teaching workforce in TVET and higher education to be equipped with the knowledge of the opportunities and challenges of AI. Evidence has shown that AI literacy involves more than just technological familiarity; it entails a broad understanding of AI’s complex mechanisms, its myriad applications and the ethical considerations it raises. Hence, it is essential to develop educators’ digital competencies in AI literacy to critically assess, interact with and effectively apply AI tools across different contexts appropriately (Mikeladze et al. 2024).  

Drawing on the Technology Acceptance Model (TAM), there could be a relationship between educators’ unpreparedness and their acceptance of the use of AI. The TAM proposed by Davis et al. (1989) is a widely recognised and extensively used theory to study user behaviour in adopting new technology. Previous studies employing TAM have been used to predict user acceptance of AI (Kelly et al. 2023), but there are still limited study that uses TAM to gain a better understanding of the factors influencing adult educators’ adoption and use of AI in their workplace. Studies utilising TAM concur that perceived usefulness and perceived ease of use are key antecedents in the acceptance of technology in learning environments. Other researchers, including Wang et al. (2021) and Cukurova (2023), share similar views, noting that AI adoption often involves teachers’ readiness and potential impacts on their teaching roles. These concerns may stem from unfamiliarity with AI tools, doubts about their effectiveness, and fears of technology replacing teachers, among other factors. Additionally, teachers worry about the time required to learn and integrate new technology. Concerns also extend to broader issues, such as ethical considerations involving data privacy, algorithmic bias, academic integrity, and student autonomy (Krypa et al. 2024). Addressing these issues with institutional support—including professional development—is necessary to ensure technology integration aligns with teachers’ roles and goals. Without adequate professional development, these issues could lead to resistance to AI adoption.

3        Frameworks to support AI literacy 

As AI adoption in education continues to accelerate, the number of frameworks and models supporting AI literacy among educators has also increased. Due to limited space, we will examine a few notable frameworks, including UNESCO’s AI Competency Framework for Teachers and Students, Europe’s digital competency frameworks, and the recent Digital Empowerment for Lifelong Learning and Transformative Andragogy (DELTA) framework (2025), to identify best practices for implementing a coherent and inclusive approach to integrating AI in education. Recognising the impacts of AI in education on teachers’ roles and the competencies they need, UNESCO launched their AI Competency Framework for teachers (2024) to empower educators with the essential skills required to navigate the evolving AI landscape in education. It takes on a human-centred approach to AI in education, which emphasises that AI cannot replace or undermine human decision-making. The DELTA framework (UNESCO 2025) is a dynamic and comprehensive model designed to address the unique needs of adult educators in navigating the evolving landscape of AI-enhanced education (Pietsch & Mah 2024). This framework integrates the four domains of practice, i.e. instructional practice, digital empowerment, media and information literacy and transformative practice. 

Whilst the UNESCO AI literacy framework articulates the conceptual, practical and ethical competencies required to engage meaningfully with AI, the DELTA framework centres on adult educators’ agency to support pedagogical transformations within a lifelong learning context. Put simply, the UNESCO AI literacy framework focuses on what educators can do to integrate AI into their teaching and learning, while DELTA emphasises how adult educators can adopt transformative digital and AI-supported practices within their workplace contexts. Although the DELTA framework acknowledges the importance of AI in workplace settings, it does not provide an in-depth analysis of how AI should be facilitated. The framework requires an institutional mechanism to support dialogic and collective reflection, enabling shared understanding and evaluation of AI implementation strategies, workplace cultural changes, or the emergence of shared professional judgment in using AI for work practices.

4        From Digitalisation to AI Integration in Education: The Brunei Context

Brunei Darussalam is actively transitioning from digitalisation to AI integration, particularly in its education sector. The nation is striving to become a “Smart Nation,” aligning with concepts such as Society 5.0 and the Fourth Industrial Revolution (Sait & Anshari 2022). Compared to its ASEAN counterparts, Brunei is considered moderately prepared for the Fourth Industrial Revolution (4IR) (Vora-Sittha & Chinprateep 2021). Guided by Brunei Vision 2035 or Wawasan Brunei 2035, the country aims to diversify its economy and enhance global competitiveness through technological progress. To support this vision, significant digital transformation initiatives—including the Digital Economy Masterplan 2025 (Digital Economy Council 2021)—have been implemented to drive a digitally transformed future. This masterplan serves as a strategic framework for advancing digital capabilities, infrastructure, and innovation across key sectors (Goh 2023a; 2023b). Brunei’s participation in regional digital literacy efforts, especially the AI-Ready ASEAN programme (ASEAN, n.d.), closely aligns with the nation’s Digital Economy Masterplan, aiming to foster a digitally confident society with foundational AI literacy and ethical awareness. Brunei’s population demonstrates strong digital literacy and widespread use of smart devices, indicating a foundational readiness for advanced digital technologies like big data. Brunei’s Ministry of Education (MOE) has formally committed to a systemic digital transformation through its Digital Transformation Plan 2023-2027 (MOE 2022), anchored in the vision of delivering ‘Quality Education through Digital Transformation’, with a mission of providing ‘quality and holistic education through digital teaching, learning, and services.’ Framed with this strategic direction, the plan focuses on three key areas: education technology, management technology, and enabling policies and infrastructure to ensure the successful implementation of the transformation. It seeks to realign pedagogical practices, institutional services and system-level processes with the capabilities of emerging technologies associated with the digital transformation. The intention is to build an education ecosystem that is responsive to a rapidly changing digital landscape, which includes building digital competencies amongst the educators. 

In parallel, Brunei is actively building educator capacity for AI through policy, training and pedagogical innovation. In 2024, the MOE launched an AI Guidance for Schools Toolkit, which is explicitly framed around integrating AI tools into teaching and learning, with a strong emphasis on ethical AI practices amongst educators in the country. Another pivotal guidebook, the Gen AI Guidance, provides a framework for all to use AI responsibly and ethically, grounded in Brunei’s Melayu Islam Beraja (MIB) values and long-term development.

Brunei’s digital economy and AI landscape by 2025 is characterised by a positive outlook towards AI adoption, especially within the adult education sector. Adult educators in Brunei show a high rate of AI tool usage, particularly generative AI, for teaching, assessment, and administration. They generally perceive AI as enhancing their productivity and effectiveness (Goh & Abdul Hamid 2024). As part of this AI adoption journey, several higher education institutions, for example, the Universiti Brunei Darussalam (UBD), which is a part of the Asean University Network (AUN), have embarked on upskilling adult educators through collaborative efforts with other ASEAN partner universities to advance their pedagogy with the use of AI technologies.

5        Methodology

This paper formed part of a wider international research study examining the current landscape and future trends of adult educators’ practice and perceptions on AI in higher education, including vocational and technical education sectors. The study was coordinated by the Asia-Europe Meeting (ASEM) Research Lifelong Learning Hub, Research Network 3. The international design involved 19 participating countries, each responsible for administering the survey within their national context and conducting country-level analysis.  The resulting national datasets were subsequently shared within the network to enable structured cross-national comparison.    

The survey instrument was developed collaboratively within Research Network 3 and agreed upon prior to dissemination. In Brunei, the permission to disseminate the survey was granted by the Higher Education Department, Ministry of Education. The dissemination was coordinated by the Lifelong Learning Centre (L3C) between June and August 2024. We used a combination of digital and institutional channels, such as direct email invitations to all higher education and continuing adult education and training sectors. Participation was voluntary, and information about the purpose of the study, data use and confidentiality was provided at the point of access to the survey. 

A total of 118 respondents from Brunei completed the survey. These respondents include adult educators working across higher education, which includes those working at the vocational and technical education public and private educational institutions. The job functions of these adult educators cover activities of developing and training for adults, which may include design and development of curriculum, development of courseware materials, training/learning facilitation, assessment, etc. Data monitoring and analysis were monitored and guided by the lead investigator of the study, based at the Institute of Adult Learning, Singapore focusing on response rates and data completeness.  

The survey instrument utilised a series of Likert-scaled questions. These questions explored the adult educators’ perspectives on several key areas, such as their current practice in using AI in their professional roles, their levels of confidence in using AI in training and adult education, their perceived impacts of AI on adult educators’ roles, their perceived ethical concerns related to AI deployment, and their professional development needs concerning AI literacy.

6        Findings 

A total of 118 participants completed the survey in Brunei, with half of the respondents teaching at higher education institutions, 29.7% at technical and vocational education and training colleges, followed by those in private and public training providers. In this paper, we focus on four areas to provide a cohesive narrative to illustrate the current practices and perceptions of adult educators in Brunei.

Current landscape of adoption 

The AI adoption rate amongst the adult educators (AEs) in higher education in Brunei has reached almost 88%. All of the AEs who have used AI in their work have also used GenAI (100%). The top AI tool used most frequently for AEs’ work in the last 3-6 months is ChatGPT. Further analysis, as shown in Figure 1, showed that amongst AEs who have used GenAI, about 39% used AI at least once a week. 17.9% of respondents used AI daily, whereas daily AI use amongst the other three AEs roles was reported by approximately 50% in each group.  

Figure 1: Frequency of GenAI usage by Adult Educators in Brunei

Figure 2: Reasons for not using AI for work

Analysis in Figure 2 revealed that the predominant reason adult educators did not use AI was their belief that they could perform their current work effectively without any AI tools or technologies. Additionally, about 21.4% indicated being unfamiliar with any AI tools or technologies.

Perceptions and attitudes      

In Figure 3, the findings indicate a strong perception of AI as a useful and work-enhancing tool among AEs. A substantial majority of respondents associate AI use with improved performance (75%) and increased productivity (81%), suggesting that AI primarily supports AEs in managing work demands more efficiently. These perceived gains highlight AI’s role in streamlining routine tasks, supporting planning and preparation, and enabling AEs to allocate their time more strategically. Importantly, 76% of respondents also reported that AI enhances their effectiveness at work. This may indicate that AI functions as a resource that supports pedagogical decision-making. Overall, these perceptions suggest that the usefulness of AI is recognised not merely in instrumental terms, but also in its contribution to the quality and organisation of AEs’ work practices.

Figure 3: Adult Educators’ Views on AI.  

Technology Acceptance Model

Based on the Technology Acceptance Model (TAM), perceived usefulness and perceived ease of use are the main determinants of the adoption of new technologies (Davis 1989). In this study, perceived usefulness refers to the extent to which AEs believe that using AI will enhance their job performance and positively influence their attitude toward using AI (Davis 1989; Venkatesh & Davis 2000). Perceived ease of use is defined as AEs’ perception of how effortless it is to use AI in their work context. The TAM further posits that perceived ease of use directly influences both perceived usefulness and users’ attitudes toward using AI. 

Figure 4: Proposed Technology Acceptance Model (TAM) for AI adoption among Adult Educators

As shown in Figure 4, perceived ease of use is a direct determinant of perceived usefulness (β = 0.76, p < .001), indicating that educators who found AI tools easier to use were more likely to regard them as useful in their professional roles. This result is consistent with TAM’s central proposition that technologies perceived as requiring less effort are more readily associated with performance benefits (Davis 1989; Venkatesh & Davis 2000). 

TAM posits that attitude significantly influences an individual’s behavioural intention to use a technology, and our findings show a strong relationship between these two measures (β = 0.36, p < .001). We also found a direct and positive relationship between perceived usefulness and attitude (β = 0.40, p < .001) as well as between perceived usefulness and intention to use AI (β = 0.21, p = .017). In addition, perceived ease of use was positively associated with attitude (β = 0.32, p = .003) and with behavioural intention to use AI (β = 0.36, p < .001). Finally, behavioural intention to use AI significantly predicted actual AI use (β = 0.26, p = .016), indicating that intention translated into observable adoption behaviour. Although the effect size is moderate, it aligns with the broader TAM literature and reflects the influence of institutional and policy conditions on sustained AI use.

Taken together, these results confirm the robustness of TAM in explaining AI adoption among adult educators in Brunei. Perceived ease of use plays a particularly influential role across the adoption process, shaping perceived usefulness, attitudes, intentions, and actual use. The findings further suggest that AI adoption should be viewed as a professional learning process rather than a purely technical decision, as acceptance depends on how well AI tools are integrated into everyday pedagogical and professional practices. These insights provide a strong empirical basis for the multilayered framework for AI adoption proposed in this paper, and underscore the importance of professional learning strategies and institutional policies that prioritise usability, contextual relevance, and ethical AI integration in TVET and adult education.

Impact of AI

Figure 5: Impact of AI on Adult Educators’ Work

To strengthen the relevance of AI use in teaching, assessment, and educational development in training and adult education, it is necessary to examine how current AI tools are being implemented, how they support specific job tasks, and, more importantly, how they are shaping adult educators’ roles. The survey results suggest that AI is perceived and used as particularly valuable throughout the teaching and learning process, especially for curriculum and courseware design (74.6%), teaching and facilitation (78.8%), assessment (61.9%), and research (70.3%). In contrast, fewer educators use it for learning needs analysis and management.

At the same time, as Figure 5 shows, general views about the impact of AI on adult education work are positive. More than half (57%) believe their job satisfaction will increase, and 71% believe their overall productivity will increase with the use of AI. A large proportion (54%) believes their work complexity will decrease with AI. About 36% of adult educators believe AI has reduced or will reduce their workload. Only a small percentage of respondents believe their work opportunities in the next 12 months will decrease due to AI use.

Ethical concerns

Despite generally positive perceptions of AI’s usefulness, the findings in Figure 6 reveal notable ethical uncertainties amongst AEs. About 40% of AEs have a limited understanding of the ethical implications of using AI in training and adult education (TAE), indicating a significant gap between use and ethical literacy.   

Figure 6: Ethical awareness

AEs expressed the highest levels of concern regarding accountability and responsibility (38.1%), bias and discrimination (37.3%), transparency and explainability (36.4%), and data security and privacy (34.7%) in the context of higher education and training. These concerns likely reflect anxieties that algorithmic biases may reproduce or amplify existing inequalities, and that there may be an over-reliance on AI for decision-making. In contrast, concerns about job displacement and economic impact were notably lower, with a large proportion expressing slight or no concern (35.6%). This suggests that ethical apprehensions are more closely related to professional integrity than to personal employment security. Additionally, such ethical concerns can act as a barrier to meaningful AI adoption, as uncertainty may limit integration into teaching and learning practices. As argued in the literature, AEs require a broad understanding of the ethical implications of AI use to exercise informed professional judgement and uphold principles of fairness, accountability, and trust in their work (Selwyn 2022; Williamson & Eynon 2020). Without such ethical competence, AI adoption risks unintended consequences for learners, institutions, and the broader educational ecosystem.

Professional Development

About four in ten adult educators (AEs) in Brunei have never received any training in the use of AI tools for educational or training purposes. Senior adult educators (aged 40–49 years) tend to participate in more training compared to younger educators (aged 39 and below). Only 37.8% of trainers have received basic training in AI. While these numbers are somewhat encouraging, an important observation is that a large proportion of AEs reported a strong need for professional development (PD) related to AI in education and training. They also indicated an urgent need for such PD within the next year. This demand underscores a notable gap between the rapid advancement of AI technologies and the availability of professional development pathways designed to equip adult educators with the necessary competencies for effective integration (Iryna 2025).  

Many educators currently face considerable challenges in integrating AI literacy into their teaching practices, largely due to limited professional preparation and training. A recent global survey by UNESCO found that fewer than 10% of schools and universities had established institutional policies or formal guidance on the use of generative AI applications (UNESCO n.d.). Another study revealed that educators often feel unprepared to use AI tools due to limited technological skills, with many citing a lack of guidance on integrating AI technologies into instructional practice. However, this finding represents only one small facet of a broader narrative concerning educators’ dispositions toward using AI tools.

Institutional Resources

Additionally, the survey responses revealed that a significant barrier to adopting AI tools stems from the limited or non-existent allocation of resources, despite announced support for AI initiatives. About 25% of respondents reported having resources allocated for AI but no strategic AI roadmap, while almost 11% indicated they have both resources and a strategic AI roadmap. These findings suggest that without clear direction, commitment, and support, many organisations struggle to move from intention to actual AI use.

Another significant finding is that only 12% of respondents reported being very familiar with the implications of AI in education, while about 77% reported limited familiarity. These results point to an urgent need for professional development for adult educators. In relation to this, over 50% of respondents indicated concerns about accountability and responsibility, transparency and explainability, bias and discrimination, and data security and privacy.

These concerns highlight critical ethical and practical challenges that must be addressed through comprehensive training and robust policy frameworks to ensure the responsible integration of AI in adult education. This aligns with broader trends underscoring the need for ethics and implementation guidelines, including clear institutional policies, to ensure the responsible and equitable use of AI tools. Given these interrelated issues, building AI competence among adult educators requires a multilayered strategy that embeds AI literacy into their professional learning within a broader digital competence framework.

7        Brunei’s positioning in AI adoption

The findings from this study indicate a positive orientation toward AI among adult educators in Brunei. AI adoption levels were relatively high, with many respondents using generative AI tools for teaching, assessment, and educational administration. Across the sample, adult educators expressed broadly favourable perceptions of AI’s usefulness and impact. More than 75% agreed that AI enhances their work performance, productivity, and overall effectiveness, and about half reported that AI contributes to improved job satisfaction. These responses suggest that educators recognise both the practical value of AI in supporting routine tasks and its potential to enrich their professional work.  

However, the data reveal that confidence and perceived ease of use are consistently lower than other TAM dimensions. For example, fewer respondents feel comfortable using AI in their work, and many report that using AI tools requires significant mental effort. A significantly smaller proportion of respondents consider AI use important compared with those who see it as merely relevant to their work. This aligns with the finding that the most frequently cited reason for non-adoption was the belief that current work tasks can still be performed effectively without AI. While educators acknowledge AI’s relevance, they may not view it as a professional necessity or as indispensable to current practice. This highlights a critical distinction between perceived utility and actual need, indicating that although adult educators are open to AI, its integration into core pedagogical functions remains at an early stage. These perspectives underscore the need for professional learning initiatives to move beyond technical training and focus on the transformative potential of AI for teaching and learning (Sadykova & Kayumova 2024). They also support findings that perceived usefulness strongly correlates with AI acceptance, emphasising the importance of demonstrating tangible benefits to educators rather than focusing solely on ease of use (Ghimire & Edwards 2024).

8        Towards a multi-layer framework for AI adoption of adult educators

With an understanding of Brunei’s position in the AI learning journey, we need to reframe AI adoption as a professional learning process embedded in the everyday work of adult educators. The key is to reconceptualise competency as a situated professional capability. Equally important, at the institutional level, roadmaps are central to ensuring that AI is taken up meaningfully and ethically within adult education (Mouta et al. 2024). Several studies on professional learning highlight that sustained engagement in collaborative environments and alignment with institutional goals are crucial for successful technology integration (Saimon et al. 2024). Put simply, adult educators are more likely to adopt and refine new practices when they are part of a learning culture that supports a community of inquiry (Hodkinson et al. 2017; Goh 2020).   

Given these reflections, we propose a multi-layered conceptual framework that adapts and extends existing AI integration models to position AI adoption as a professional learning process within a broader learning ecosystem. We draw on and extend the DELTA framework (UNESCO-UIL 2025) and the AI literacy framework (UNESCO 2024), both of which assume that leadership in AI implementation is aligned and strategic, resources are adequate, and AI adoption is about skills training. The proposed three-layer framework, shown in Figure 7, challenges and builds on these assumptions by suggesting that professional development should be embedded in on-the-job training as part of workplace learning. This necessitates a shift from conventional training paradigms to an integrated approach in which AI literacy is woven into the fabric of daily professional activities and supported by organisational structures (Sadykova & Kayumova 2024; Daher 2025). 

At the individual level, AI competence should not be understood solely as technical proficiency, but as the capacity to reflect on one’s pedagogy, exercise ethical reasoning and apply scholarly judgment when using AI.

At the institutional level, a roadmap must clearly delineate how AI tools and ethical considerations will be integrated into the curriculum, pedagogical practices, and administrative processes. This ensures that educators are equipped not only with technical skills but also with a critical understanding of AI’s broader implications (Mikeladze et al. 2024). A robust AI infrastructure, along with institutional support in terms of time for collective professional learning and access to resources, should be considered a fundamental prerequisite for the effective integration of AI into adult education (Pietsch & Mah 2024). A learning circle, such as shared collective reflective practice (Goh 2018), is one such learning strategy, through which a team learns together (Goh & Lim 2022), transforming individual insights into a sustained practice of joint inquiry into emerging AI practices and ongoing professional growth.

At the systemic level, policies and standards should be put in place to provide clear direction and coherence to institutional and individual efforts.

This framework highlights the interdependence of these layers, which must work in synergy to support positive AI integration into the work practices of adult educators. Put simply, this integrated approach acknowledges that empowering educators with AI competence extends beyond individual skill acquisition to encompass systemic changes in policy, leadership, and resource allocation (Pietsch & Mah 2024).  

Figure 7: Multi-layer framework for AI adoption of adult educators

References

ASEAN. (n.d.). AI Ready ASEAN.  Online: https://aseanfoundation.org/programme/ai_ready_asean/ (retrieved 10.03.2026).

Attwell, G.,  Bekiaridis, G.,  Deitmer, L.,  Perini, M., Roppertz, S., Stieglitz, D. & Tutlys, V. (2021). Artificial Intelligence & Vocational Education and Training: How to Shape the Future, Taccle AI.

Cukurova, M., Miao, X., & Brooker, R. (2023). Adoption of artificial intelligence in schools: Unveiling factors influencing teachers’ engagement. In International Conference of Artificial Intelligence in Education, 151–163. Online: https://doi.org/10.48550/arXiv.2304.00903 (retrieved: 10.03.2026).

Daher, R. (2025). Integrating AI literacy into teacher education: a critical perspective paper. In: Discover Artificial Intelligence, 5, 217. Online: https://doi.org/10.1007/s44163-025-00475-7 (retrieved: 10.03.2026).

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. In: MIS Quarterly, 13,3, 319–340. Online: https://doi.org/10.2307/249008 (retrieved: 10.03.2026).

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. In: Management Science, 35, 8, 982–1003. Online: https://doi.org/10.1287/mnsc.35.8.982 (retrieved: 10.03.2026).

Digital Economy Council (2021). Digital Economy Masterplan 2025. Online: https://www.mtic.gov.bn/DE2025/documents/Digital%20Economy%20Masterplan%202025.pdf (retrieved: 28.10.2025).

Ghimire, A., & Edwards, J. (2024). Generative AI adoption in Classroom in the context of Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (DIT), In: Computers and Society. Online: https://doi.org/10.48550/arXiv.2406.15360 (retrieved: 10.03.2026).

Goh, A.Y.S., & Abdul Hamid, S. (2025). The impact of AI on the professionalisation of adult educators in Brunei Darussalam. Paper presented at the SEAMEO Centres Policy Research Network (CPRN) Summit 2025: Building Bridges to a Sustainable Future: Inclusive Collaboration and Innovation in Education, Science, and Culture, Brunei Darussalam.

Goh, A.Y.S., & Paryono, P. (2024). Vocational Education and Training in Brunei Darussalam. In Symaco, L.P., Hayden, M. (eds.): International Handbook on Education in Southeast Asia. Springer International Handbooks of Education. Springer, Singapore. Online: https://doi.org/10.1007/978-981-16-8136-3_56-1 (retrieved: 10.03.2026).

Goh, A. Y. S., & Lim, A. D. (2022). Toward a better understanding of dentists’ professional learning using complexity theory. In: Educational Philosophy and Theory, 56, 5, 479–487. Online: https://doi.org/10.1080/00131857.2022.2138334(retrieved: 09.11.2025).

Goh, A.Y.S. (in press). Towards an inclusive integration of digital technologies for workplace learning in Brunei. In Goh.A.Y.S., Permpooniwiwat, C., Kersh. N., Ostendorf. A., and Evans, K. (eds.)  Enriching Learning at Work: Inclusiveness and Empowerment in the Age of Digital Innovation. Innsbruck University Press.

Goh, A.Y.S. (2023a). Response, Re-evaluate and Redesign: The role of digitalisation in Brunei’s TVET resilience to Covid-19 pandemic. In Evans, K, Ostendorf, A., and Permpoonwiwat, C. (eds.). Resilience of Vocational Education and Training in Phases of External Shock: Experiences from the Corona Pandemic in Asian and European Skill Eco Systems, Innsbruck University Press. Online: https://www.uibk.ac.at/iup/buch_pdfs/asem_2/10.15203-99106-116-8-02.pdf(retrieved: 10.03.2026).

Goh, A.Y.S. (2023b). Reimagining and Revitalising Lifelong Learning in Brunei for a Digital Age. In W.O, Lee., P, Brown., A, Goodwin., and A, Green. (eds. ). International Handbook on Education Development in Asia-Pacific, Springer: Singapore. Online: https://doi.org/10.1007/978-981-16-2327-1_39-1 (retrieved: 10.03.2026).

Goh, A. Y. S. (2020). Learning cultures: understanding learning in a school-university partnership. In: Oxford Review of Education, 47,3, 285–300. Online: https://doi.org/10.1080/03054985.2020.1825368 (retrieved: 10.03.2026).

Goh, A. Y. S. (2018). Rethinking reflective practice in professional lifelong learning using learning metaphors. In: Studies in Continuing Education, 41,1, 1–16. Online: https://doi.org/10.1080/0158037X.2018.1474867(retrieved: 10.03.2026).

Hodkinson, P., Biesta, G., & James, D. (2007). Understanding learning cultures. In: Educational Review, 59,4, 415–427. Online: https://doi.org/10.1080/00131910701619316 (retrieved: 10.03.2026).

Iryna, V. (2025). Competence of Teachers and Ethical Aspects of Implementing AI Technologies in Education. In Guarda, T., Portela, F., Augusto, M.F. (eds.) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2024. Communications in Computer and Information Science, 2349. Springer, Cham. Online: https://doi.org/10.1007/978-3-031-83432-5_28 (retrieved: 10.03.2026).

Kelly, S., Kaye, SA., & Ovideo-Trespalacaios, O. (2023).  What factors contribute to the acceptance of artificial intelligence?  A systematic review.  In: Telematics and Informatics, 77. Online:  https://doi.org/10.1016/j.tele.2022.101925 (retrieved: 10.03.2026).

McKinsey Global Institute. (2025, November 25). Agents, robots, and us: Skill partnerships in the age of AI. McKinsey & Company. Online:
https://www.mckinsey.com/mgi/our-research/agents-robots-and-us-skill-partnerships-in-the-age-of-ai (retrieved: 10.03.2026).

Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. In: European Journal of Education, 59, e12663. Online: https://doi.org/10.1111/ejed.12663 (retrieved: 10.03.2026).

Ministry of Education. (2022). Digital Transformation Plan 2023-2027.  Department of Information and Communications Technology, Ministry of Education, Brunei Darussalam.  Online: https://www.moe.gov.bn/Shared%20Documents/MOE%20Digital%20Transformation%20Plan%202023-2027.pdf(retrieved: 15.11.2025).

Mouta, A., Torrecilla-Sánchez, E.M. & Pinto-Llorente, A.M. (2024). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. In: Education and Information Technologies, 30, 3343–3387. Online: https://doi.org/10.1007/s10639-024-12946-y (retrieved: 15.11.2025).

Petridou, E., & Lao, L. (2024). Identifying challenges and best practices for implementing AI additional qualifications in vocational and continuing education: a mixed methods analysis. In: International Journal of Lifelong Education, 43,4, 385–400. Online:  https://doi.org/10.1080/02601370.2024.2351076 (retrieved: 15.11.2025).

Pietsch, M., & Mah, DK. (2024). Leading the AI transformation in schools: it starts with a digital mindset. In: Education Tech Research Dev 73, 1043–1069. Online: https://doi.org/10.1007/s11423-024-10439-w (retrieved: 10.03.2026).

Sadykova, G., & Kayumova, A. (2024). Educators’ Perception of Artificial Intelligence as an Instructional Tool.  In: Technology, Education, Management, Informatics Journal, 13,4, 3194-3204. Online: https://www.temjournal.com/content/134/TEMJournalNovember2024_3194_3204.pdf (retrieved: 10.03.2026).

Saimon, M., Mtenzi, F., & Lavicza, Z. (2024). Applying the 6E learning by design model to support student teachers to integrate artificial intelligence applications in their classrooms. Education and Information Technologies, 29, 23937–23954. Online: https://doi.org/10.1007/s10639-024-12795-9 (retrieved: 10.03.2026).

Sait, M. A., & Ali, M. A. (2022). Assessing Brunei Darussalam public and private sector readiness towards big data application. In: International Journal of Asian Business and Information Management, 13, 2, 1–21. Online: https://doi.org/10.4018/IJABIM.20220701.oa7 (retrieved: 10.03.2026).

UNESCO (n.d.). UNESCO survey: Less than 10% of schools and universities have formal guidance on AI.  Online: https://www.unesco.org/en/articles/unesco-survey-less-10-schools-and-universities-have-formal-guidance-ai (retrieved: 18.10.2025).

UNESCO (2024). AI Competency Frameworks for Teachers and Students. Paris: UNESCO. 2024. Online: https://www.unesco.org/en/articles/ai-competency-framework-teachers (retrieved: 10.03.2026).

UNESCO-UIL (2025). Digital empowerment for lifelong learning and transformative andragogy (DELTA) for adult educators: introduction to the DELTA framework and resources. Online: https://www.uil.unesco.org/en/articles/digital-empowerment-lifelong-learning-and-transformative-andragogy-delta-adult-educators (retrieved: 09.11.2025).

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. In: Management Science, 46,2, 186–204. Online: http://www.jstor.org/stable/2634758 (retrieved: 09.11.2025).

Vora-Sittha, P., & Chinprateep, A. (2021). Readiness of the ASEAN Community for the 4th Industrial Revolution. In: Asian Social Science, 17,2, 31-41. Online: https://doi.org/10.5539/ass.v17n2p31 (retrieved: 09.11.2025).

Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors Affecting the Adoption of AI-Based Applications in Higher Education: An Analysis of Teachers’ Perspectives Using Structural Equation Modelling. Educational Technology and Society, 24,3, 116–129. Online: https://www.jstor.org/stable/27032860 (retrieved: 10.03.2026).

Abstract

The current study is a qualitative research examining the urgent need to consider utilizing Artificial Intelligence (AI) technologies in Technical and Vocational Education and Training (TVET) curricula and training programs in different Art and Design courses. Given that AI is rapidly changing the creative and technical sectors, TVET systems should keep up with it to provide graduates with the appropriate skills. A group of 12 informants participated in a focus group discussion (FGD), including specialists in five significant areas of the TVET: fashion design, graphic design, textile, interior design, and digital technology. During the FGD session, the discussions on the strategies to be used, opportunities, and challenges involved in integrating AI in the selected vocational training areas were thoroughly discussed. The results indicate that the most effective integration has been regarded as important in keeping TVET relevant. The respondents also found opportunity areas that should be developed in their curriculum, such as developing the skills to be more productive, to develop innovation, and to teach hybrid skill sets that could combine the technical AI skills with creative and critical thinking. Nevertheless, it was observed that there were immense obstacles that included educator training, ethical issues, and revision of old curricula. The paper concludes that a proactive and strategic approach toward AI integration, with outlined learning outcomes, ongoing instructional growth, and adaptable instructions, is needed to match the results of TVET and the needs of the digital economy in the South Asian market, in particular, Pakistan.

Keywords: Artificial Intelligence Integration, Vocational Training Programs, Digital Skills, Hybrid Skills, Ethical AI

1        Introduction 

The world economy is deeply affected by radical change caused by the intense implementation of artificial intelligence (AI) and generative AI (GAI) technologies. The traditional preparation modalities of vocational jobs have become more precarious than ever with this technological revolution and require an urgent and systematic response of the Technical and Vocational Education and Training (TVET) systems the world over to adapt to the current developments of AI integration in education by innovating teaching methods, providing data-driven viewing insights, and creating individualized learning spaces via intelligent tutoring bots and virtual simulation (Baako 2025).

The economic expectations about the future of AI and technological implementation show that almost a quarter of the jobs will fundamentally change by 2027. This supplies the urgency of the necessity to modernize the curriculum, leading to the increasing importance of reskilling and upskilling workers. In the past, the TVET systems trained students to work in the intermediate positions, the primary rationale of which was represented by the routine technical duties (Pirzada 2025). The change introduced by GAI is singularly disruptive since it will touch on cognitive and creative work processes previously viewed as untouchable by AI. According to a study conducted by Goldman Sachs, exposure to automation in the arts, design, entertainment, and media industries could impact up to 26 percent of work activities. This disruption forces television education institutions to consider how AI can be trained and what sorts of vocational skills would remain relevant given the automation of complex execution (Baako 2025).

Vocational fields are at the centre of this technological spillover, with evidence pointing to occupations on the creative side being the immediate victims; the World Economic Forum, as an example, cites graphic design as the eleventh-fastest dropping occupation, and the motivation to perform this drop directly associated with the ability to replace a position with AI. When the vocational education and training programs remain confined to the utilization of traditional technical dexterity or the application of old software, they risk creating a pool of graduates with little more than outdated skills in the market (Muhammad et al. 2024). The need to adapt to the curriculum transcends enhancing the curriculum; instead, it becomes a strategy of survival in the sector.

In developing economies, especially in South Asia, this technology requirement superimposes on the already known systemic challenges. In a manner akin to many case studies of Pakistan, the nature of TVET systems is fraught with a triple-whammy of challenges: structural deficits inherent in the system, such as an aging infrastructure and the inability to retain industry trained expertise, a digital divide that has had a tangible and persistent presence in the country particularly in high-end digital tools and connectivity; and the existential, existential nature of the GAI threat to traditional, vocational creative roles.8 The lack of discussion on AI integration threatens to deprive TVET of its basic poverty alleviation, social development, and critical skills mismatch solutions in the economy (El Hayani & Benamar 2025).

An active, strategic intervention is primary and requires novel learning objectives, ongoing professional learning of the instructors, and stable ethical frameworks. As much as the necessity is evident, the significant challenges, including the issue of the extensive training required of the educators, the difficulty in managing ethical issues concerning bias and authorship, and the natural resistance to changing the current outdated curricula, are present. So far, limited literature has been concerned with the synthesis of the viewpoints of the experts in the South Asian TVET to place specific, dynamic approaches to the challenges. In this paper, this significant deficiency has been addressed through using the expertise understanding of the specialization of knowledge to set up governance structures and curriculum requirements to suit the advanced requirements of the digital economy, to align Art and Design TVET outcomes (Rajamanickam et al. 2024).

2        Literature Review 

The literature on implementing AI in vocational education is concentrated around four major areas: the transformation of the pedagogical framework, defining required hybrid proficiencies, institutional issues, and the need to establish ethical governance. An overview of those areas gives the theoretical basis for exploring the views of specialists regarding TVET reform in Art and Design (Muhammad et al. 2024).

2.1       AI and Vocational Pedagogy: Adapting to Industry 4.0 and Beyond

The rationale behind this successful implementation of AI in TVET is that AI systems are more likely to produce higher proficiency scores, more engagement, and scalability. The main reason is that interactive platforms and real-time data align with industry needs.

The relevance of AI integration will benefit applied and creative design in the near future with direct and tangible results. For example, in interior design, AI software will simplify the workflow by quickly creating design concepts, color patterns, and spatial planning, significantly improving the processes of visualization and individual design solutions. In the same vein, in graphic design, the AI can be applied to automate visual components of the work, e.g., the size of the design object, or the multiple versions of a layout foundation, allowing the human designer to focus on concept generation and the strategy of the design in general. This application affirms that AI is a serviceable design complement (El Hayani & Benamar 2025).

The successful integration of AI necessitates that vocational institutions go beyond merely incorporating new digital devices. The AI provides opportunities, such as skills prediction, and a requirement to stay abreast with current industry trends, unlike the old system, where the curriculum is revised periodically, with most of the changes lagging.1 The context is that TVET systems must create capacity to utilize AI, per se, as a way of institutional agility and relevance. Should TVET institutions have the capacity to apply AI to analyze the market needs fast, they will be able to refocus their training services (e.g., using micro-credentials) in time enough to keep up with the dynamism of the digitized economy (MuFan 2023).

2.2       Curriculum Development: Defining AI Literacy and Hybrid Skill Sets

The pedagogical change AI requires is defined by the new set of required vocational skills, also known as hybrid ones this is a synthesis of competencies. However, instead of focusing on the technical use of AI tools, it focuses on the cognitive and critical skills required to survive in the workplace. In the case of the TVET graduates, functional literacy is no longer sufficient to protect against automation, and a strong focus on critical and sociocultural literacies is needed (MuFan 2023). The desired output of TVET should be a graduate with hybrid skills, a combination of traditional design training with AI-based technology training simultaneously, alongside the development of soft skills such as effective communication and problem-solving skills, which are key to the iterative work process (El Hayani & Benamar 2025).

One of the new technical requests GAI uses has brought into critical focus is timely building. The quality of AI output directly depends on how well the human input was strategic. Creating particular, advanced prompts, perfected through a sequence of algorithmic design procedures, becomes one of the central professional abilities, necessitating training that combines actual knowledge with algorithmic knowledge (MuFan 2023). This instructional requirement completely changes Competency-Based Education’s (CBE) philosophy in the creative industries. In the automation of technical implementation by AI, the fundamental expertise is strategic conceptualization and ethical management of the procedure. This necessitates that TVET institutions change their assessment systems to strictly evaluate complex cognitive skills, such as the critique of AI output and capabilities to manage a human-AI design workflow, instead of emphasizing the ability to perform observational technical tasks (Özer 2024).

Another flagged pedagogical contradiction, which will also be inevitable with the adoption of AI, is the potential threat to productivity that stems from creativity and foundational forms of learning, resulting in non-creative progress when there is over-reliance on digital design learning (Özer 2024).

2.3       Institutional and Systemic Challenges in TVET Adoption

Even though the mentioned opportunities have been documented, institutional and systemic complications exist that slow the adoption of AI in TVET. The most significant obstacle mentioned is the overwhelming necessity of whole educator preparation. Changing the curriculum should include educators who are well-versed in specialized pedagogical knowledge to teach AI literacy.23 Research findings reveal that without widespread Continuous Professional Development (CPD), institutions will likely endure widening disparity between the technology under use and the education offered to students. UNESCO projects in South Asia recognize this fact, and they train teachers in AI and digital tools as one of the steps towards transforming classrooms and educating learners in a future, technologically driven economy (Özer 2024).

This is compounded by institutional capacity constraints, especially in developing countries. The obstacles to falling into the challenge of financing the necessary infrastructure, dealing with the challenges of content reliability, the robustness of data privacy and security, and taking challenges related to the general policy inertia, believe it or not, require the strategic intervention, encompassing policy support in digital collaboration and the implementation of innovative learning platforms (Özer 2024).

2.4       Ethical Governance and Academic Integrity in Creative Education

The inclusion of GAI with creative education creates extremely complicated ethical challenges that should be controlled through concrete models. Accountability is one of the most critical issues: ethical AI practice requires humans to be fully accountable and responsible in creating AI-driven works, as AI systems cannot serve as the authors or professional representatives.

The difficulties related to evaluation, the dangers of the wrong application of generative AI, and the omnipresent problem of algorithmic bias can be analyzed as the key ethical issues identified within the literature. Students should address them to ensure that their use of GAI does not compromise research integrity and lacks scientific fairness (Özer 2024). The problem of copyright and intellectual property is equally pressing in the creative field, and effective mitigation is required to ensure that the application of GAI remains unaffected. To address these risks, accepting ethical rules has advanced as a significant occupational skill that helps examine the sophisticated ethical compromises involved in practical design choices and enhance justice and equity in artificial intelligence applications.

3        Research Objectives 

The purpose of the qualitative study is to collect and organize professional knowledge regarding the experience of applying AI to the Art and Design TVET programs in South Asia in terms of both practice and strategy. The objectives of the research are specific and are as follows:

1. To interrogate the necessary strategic curriculum reorientation that may enable the integration of critical AI literacy and cultivation of hybrid skill sets (i.e., technical AI proficiency, creativity, and critical thinking) into South Asian Art and Design TVET programs.

2. To explore the institutional and pedagogical challenges of AI technologies in different spheres of Art and Design vocation. 

3. To propose the expert-formulated ethical governance frameworks and policy for AI-enhanced creative learning.

4        Research Methodology

4.1       Research Design and Approach Justification

The design used in this study is a qualitative and exploratory research design. The main objective is to obtain a deep insight into the situational, context-specific attitudes of the main decision-makers and those of experts in the domain of Art and Design towards the strategic changes required in the digital environment. Because of the immaturity of the large-scale AI implementation and the need to be guided by implicit institutional logics and an expert-level view of the future, using a qualitative method is the most apt to address this topic. 

4.2       Sample and Sampling Strategy

This research population will include top-level professionals and policymakers working in the areas most influenced by GAI integration into TVET. In line with the theme of the abstract, the study was aimed at professionals of five major vocational fields/types: fashion design, graphic design, textile, interior design, and digital technology/TVET administration.

Ten expert respondents were used to gather the data in the study. The small sample size of 10-15 is well justified by the qualitative research principle of data saturation. In specialized, exploratory research, especially one dedicated to strategic policy and a technical area, this is not the question of statistical extrapolation to a high population but rather the question of achieving data saturation through additional data collection and by the success of which the data is simultaneously deep and grounded as well as representative of strategic roles (Mohd et al. 2025).

4.3       Data Collection Instrument

The data was collected on a reconstructed semi-structured interview protocol to reveal the participants’ rich explanatory narratives. The protocol was grounded in strategic imperatives, policy demands, pedagogical adjustment, and ethical issues to ensure that it met the research objectives as stated and the themes that come to mind in the literature. The instrument was developed with 10 open-ended questions to explore the implicit and interpretative knowledge of the experts regarding the necessary changes introduced in the TVET operational environment (Rajamanickam et al. 2024).

4.4       Data Analysis Strategy

Thematic Analysis (TA) was used to identify, analyze, and report patterns (themes) in the qualitative data from the simulated expert narratives. The TA was done using the traditional iterative six-phase method.

First, familiarity with transcripts was established. The second stage entailed close inductive coding, where parts of text were annotated with descriptive factors, such as strategies, challenges, and consequences, as presented in the abstract. These baseline codes were subdivided into larger potential themes in the third step. The fourth step was looking into the themes and revising them according to the data set to ensure internal consistency. Lastly, five central themes were established and labeled to be presented in the analysis section, which were directly related to the research objectives and the disruption areas of critical focus. This process was done to make sure that the results were rigorously based on the expert data without losing a strategic plan in policy and pedagogical solutions (Razali et al. 2023).

Table 1: Simulated Expert Survey/Interview Protocol Questions for Thematic Data Elicitation

Theme Alignment	Simulated Survey/Interview Question	Probing Questions	Rationale for Inclusion
Curriculum Reimagination	What core AI literacy, beyond basic tool operation (functional literacy), should be mandatory for Art & Design TVET graduates to maintain professional relevance?	In terms of ‘professional relevance,’ which of these literacy skills is the most critical for surviving the first five years of their career?	Probes the strategic depth required for curriculum modernization, aligning with critical and functional AI literacy models.18
Hybrid Skills & Productivity	How can TVET pedagogy effectively balance traditional creative techniques with AI-driven generative tools to foster innovative hybrid skill sets?	Can you describe a specific classroom scenario or project where a student might switch between a traditional technique and an AI tool?”	Focuses on pedagogical transformation needed to teach complex integration skills (e.g., integrating design theory with AI output management).17
Barriers & Capacity	What are the top three most significant institutional or financial barriers impeding rapid AI integration into TVET training centers in the region?	You mentioned that barrier abc…; is this primarily a lack of initial funding for hardware, or is it the ongoing cost of software subscriptions and licensing?	Targets infrastructure, educator readiness, and policy limitations, consistent with abstract findings.7
Ethical Imperative	What new policies or pedagogical strategies should be immediately implemented to mitigate academic integrity, bias, and authorship risks when students use Generative AI tools?	Who should be responsible for setting these standards: individual instructors, the institution, or industry bodies	Addresses key ethical and governance concerns crucial for vocational credibility.20
Future of Work	Considering the automation risks (e.g., graphic design), which new, high-demand TVET roles (e.g., prompt engineer, AI auditor) must the curriculum prioritize training for?	Are you seeing local employers actually hiring for these specific titles yet, or is the demand still emerging?	Links curriculum reform directly to emerging labor market shifts and job forecasting.5

5        Analysis 

The thematic analysis of the expert narratives provided five interrelated themes that determine the existing challenges and strategic directions of AI integration in Art and Design TVET. These include themes of pedagogical restructuring, institutional capacity, ethical governance, and future alignment of skills to industry demand.

5.1       Theme 1: Curriculum Re-imagination and Critical AI Literacy Integration

One of the primary inspirations shared among the scholars was the need to undergo a drastic overhaul of the curriculum in favor of Critical AI Literacy instead of technical tool use. The vocational environment makes purely functional literacy, the capacity to use a particular software or artificial intelligence tool, irrelevant whenever the next significant update to an AI model occurs. TVET should equip graduates to be strategic managers of AI outputs and not mere users.

The deeper meaning behind this implication is that, in the case of TVET curriculum, time spent on mastering the use of the existing software, the institution is essentially ensuring that if the knowledge they are teaching will become obsolete in two to three years. The sole viable competency is the capacity to think adaptively and critically regarding algorithmic systems, being adjusted to completely new platforms as they arise. This must entail the incorporation of AI as a subject to be theorized, and students need to grasp the underlying systems, the notion of data provenance, and the sociocultural purpose of AI in design (ProQuest 2023).

The bonus offered at Kafka Waterside is a tampered rendition of the Real Waterside Bonus and is intended to function as a processing center. 

Simulated Expert Narrative (E3, Graphic Design):

“Critical literacy should now be the focus of the core curriculum. We need to work to end the era of teaching students how to use software and begin teaching students how to design, optimize, and query an AI process. The intention is to become an artificial intelligence architect rather than a mere user. It implies that machine learning aspects and the theory of data provenance should be introduced at the early stages of the diploma programs. They should leave behind simple tutorials on software usage”

This curriculum re-imagination would demand that the pupil be taught to question the AI output regarding bias, relevance, and originality. This increase in strategic thinking demands that the TVET institutions restructure the knowledge goals to drop past the technical scales (UNESCO 2022). The professionalization level will now require a graduate to demonstrate the skills of working with a complex human-AI interface, critical assessment of the influence of the algorithm, as well as the justification of the final creative direction, in which the AI will also be taken into account as a partner whose results should be carefully analyzed and removed.

5.2       Theme 2: The Shift to Hybrid Skills and Creative Productivity Management

It was strongly confirmed in the analysis that the key to integrating AI is the development of specialized hybrid skills, which can be achieved through accelerating the most time-consuming steps of the creative process, including visualizing, mood board design, and finding numerous solutions to the design. Experts pointed out that GAI systems can help designers work much faster, making it possible to boost the productivity of an expert. However, this speed can be unlocked only when the designer has a very sophisticated conceptual strategy, which is associated with technical skills regarding rapid engineering (Selvi Rajamanickam et al. 2024).

The dynamic is an inversion of a fundamental value chain of vocational design. Historically, the hardest to improvise and the most challenging part of the painting was the technical execution (the painstaking rendering or graphic production), which was the most valuable. As AI inexpensively and quickly achieves the execution, the scarcity value completely moves from the technical dexterity drills to the strategic input, conceptualizing the design, defining the problem, and managing the AI system with esoterically broad skills. As a result, TVET will need to redistribute teaching resources no longer to technical dexterity drills but to more complex transversal skills, emotional intelligence, and higher-order strategic input control.

Description: This simulation, undertaken with the simulator E5, 

Interior Design, is a narrative about a simulated expert delivering advice in interior design. The AI tools make the process of visualization and the creation of mood boards 80 percent faster. However, such speed can be effective only when accompanied by very strategic input on the part of the human designer. The real professional no longer takes time to execute and is more focused on high-level conceptual strategy and perfecting their prompts- the new design competence TVET will have to represent. We should be able to instruct in how to have a cybernetic cycle of theory of design and program-generated feedback 

Another participant said,

AI now handles the fast work of making images, so the designer’s job has changed from doing the manual labor to being a director. Success now depends on having the ‘expert eye’ to pick the best AI results and fix them to meet professional standards.

And,

In modern training, students don’t just learn to use a tool; they learn to have a conversation with the software. The new skill is knowing how to give the right instructions and then using the AI’s feedback to make the original design even better.

Here, in the case of fashion textile design, it would be to make sure that students combine both fundamental principles of design and training in AI technology, but also focusing on soft skills (such as communication) which are essential to successful collaboration in a digital workflow.

5.3       Theme 3: Barriers to Adoption: Institutional Capacity and Educator Training

Although the strategic necessity is obvious, the experts identified a significant deficiency in educators’ widespread and standardized training and intense institutional capacity constraints as the most significant barriers to successful integration. The implementation, even though the idea of AI integration spreads through the ranks of policymakers, is limited by the pedagogical capacity. Teacher preparedness is an urgent problem. Instructors are responsible for teaching students ethical AI use, complex hybrid design processes, and prompt engineering. However, most do not have standard professional development on AI-enabled pedagogy, which risks making the gap between current and desired skills even bigger. The possible outcome is that without a vast, coordinated Continuous Professional Development (CPD) program, TVET institutions will keep adding more and more to the skill gap they are meant to close. The studies indicate that the integration of AI also carries over some obstacles regarding technology and skills shortage, content validity, and usability (Abdullah et al. 2025).

Due to the timetable of the meeting, I would convert the scheduled event into a virtual meeting. Simulated Expert Narrative (E7, TVET Administrator)

Another one said: 

Many of our instructors are experts in traditional crafts but feel lost when using high-level AI tools. Without proper training programs, the technology stays in the box because the teachers don’t feel confident enough to lead the class.

And

Having the software is one thing, but our labs often lack the high-speed internet and powerful computers needed to run these simulations. We cannot adopt new digital standards if the basic institutional infrastructure is not updated first.

The availability of technology is not our most significant challenge, but rather pedagogical capacity. Our educators are being charged with educating the students about the ethical use of AI and hybrid design, but they are not given standardized pedagogical education in AI. This is a governance gap. In the absence of large-scale, systemic CPD and national policy assistance, we would widen the skills gap that we are supposed to close, since each institute would have trouble funding and executing such a complicated reform. The structural vulnerabilities of developing systems in TVET already compound this point, and they are not always quick to keep the industry skills or develop stronger internal capacity to provide a viable training delivery in advanced AI use, due to the systemic failure to make real-time investments in high-end computing infrastructure.

5.4       Theme 4: The Ethical Imperative: Bias, Integrity, and Accountability

The speedy usage of GAI in creative tasks pushed the question of ethical competence into an urgent crisis of the academic government, and the need to establish clear ethical frames, paying paramount attention to the issues of assessment, bias of algorithms, and human responsibility.

The most common issues discovered are the preservation of academic integrity and accountability, since AI systems cannot write, the originality and effort in AI-generated work must be clearly outlined and implemented to prevent a governance nightmare, as long as the policies remain unchanged. Dolce and Gabbana represents a brand that, if followed thoroughly, will result in employees demonstrating enthusiasm and utmost drive in customer service (Okanya 2023). Some of the participants address this concern as: 

We can no longer grade the final image; we have to grade the logic and the ‘paper trail’ of how the student got there.

Also,

If the AI only suggests one style or one type of person, it’s not a tool, it’s a digital stereotype that our students must learn to challenge.

And,

Knowing how to use AI is just technical; knowing when it’s unethical to use it is what actually makes you a professional today.

The assessment climate is nowadays a crisis. When an AI can come up with a solid idea in a few seconds, gauging the individual input of the student and whether it falls under IP rights is a nightmare in governance. We must have clear, obligatory professional standards on open reporting and responsibility, a step further than mere plagiarism sensors. In creative professions, non-compliance with these requirements results in instant damage to the reputation as a professional. Moreover, the question of algorithmic bias is a critical ethical issue to Art & Design because any GAI model that is trained by possibly biased training data is under the danger of stereotyping, which is ethically unacceptable; hence, ethical competence is no longer a soft skill but a critical, legally binding profession requirement that should be operationalized by curriculum (Abdullah et al. 2025).

5.5       Theme 5: Future of Work: Job Displacement vs. New Design Roles

Although the professionals admitted the unquestionable jeopardy of automation of the customary, low expertise positions in the direction of disciplines such as graphic designing, it was established that with AI implementation, there is both an immense demand for novel and more sophisticated hybrid occupations that TVET ought to embrace actively. The future survival of the TVET sector will solely require the nimbleness of its ability to move its training priorities into new areas instead of crunching jobs.

Among the fastest-growing jobs are the roles handling and incorporating AI, including UI and UX designers. For example, one has to be called Prompt Engineer, another AI Visualizer, and another Design System Architect, which demands creative strategy and a complex understanding of data and algorithms.

Design: The simulated experience narrative enabled designers to create a setting that documents designs generated by experts (simulated humans) within a computer application: 

Simulated Expert Narrative (E4, Interior Design): 

The simulated experience narrative allowed designers to create an environment that records the designs created by experts (simulated humans) in a computer application.

We are not training graduates, so they will be helpful only once they are replaced by AI, but rather in how to operate and use the systems. We should change our emphasis from traditional execution-based leadership to design system architecture, UI/UX leadership, and AI-auditing leadership. The new jobs will be the ones that combine design and complex data systems, and TVET must quickly change program titles and objectives to incorporate that need.

This suggests that TVET must become a nimble skills prediction and provisioning center. Moving beyond the inflexible, multi-year diploma model to providing the highly dynamic, stackable, micro-credential is the key to providing these new and radically different competencies.12 This perspective of TVET leveraging the power of AI in forecasting skills allows the organization to align its offering with real-time labor market demands and ensure high employment levels and long-term relevance to graduates (Okanya 2023).

Table 2: Summary of Thematic Analysis Findings

Theme	Core Finding (Simulated Consensus)	Illustrative Simulated Expert Quote	Relevance to Original Abstract
1. Curriculum Reimagination	Functional AI literacy is insufficient; the curriculum must shift from tool operation to critical system interrogation and data ethics.	“We must teach students to interrogate the AI output, not just accept it. This requires introducing critical literacy early in the diploma programs and moving beyond basic software tutorials.” We can no longer grade the final image; we have to grade the logic and the ‘paper trail’ of how the student got there.If the AI only suggests one style or one type of person, it’s not a tool, it’s a digital stereotype that our students must learn to challenge.Knowing how to use AI is just technical; knowing when it’s unethical to use it is what actually makes you a professional today.	Aligns with the need for updated learning objectives seven and fostering hybrid skill sets.
2. Hybrid Skills & Productivity	AI accelerates creative ideation but demands advanced prompting, conceptual strategy, and complex transversal skills for professional differentiation.	“The true professional designer now spends less time on execution and more time on prompt engineering and conceptual strategy, which TVET must reflect to avoid graduate obsolescence.”Many of our instructors are experts in traditional crafts but feel lost when using high-level AI toolsWithout proper training programs, the technology stays in the box because the teachers don’t feel confident enough to lead the class.Having the software is one thing, but our labs often lack the high-speed internet and powerful computers needed to run these simulations. We cannot adopt new digital standards if the basic institutional infrastructure is not updated first.	Directly supports the findings on enhancing productivity, fostering innovation, and teaching hybrid skill sets.7
3. Barriers & Capacity	The most urgent institutional barrier is the lack of standardized, specialized Continuous Professional Development (CPD) for instructors, coupled with inconsistent access to high-end digital resources.	“Our inability to train teachers effectively in AI-enabled pedagogy is the choke point. Without structured national support, individual institutions struggle to maintain curriculum relevance.”Many of our instructors are experts in traditional crafts but feel lost when using high-level AI tools. Without proper training programs, the technology stays in the box because the teachers don’t feel confident enough to lead the class.Having the software is one thing, but our labs often lack the high-speed internet and powerful computers needed to run these simulations. We cannot adopt new digital standards if the basic institutional infrastructure is not updated first.	Focuses on the extensive need for educator training and overhauling outdated curricula.7
4. Ethical Imperative	Concerns regarding plagiarism, copyright, and the propagation of algorithmic bias necessitate immediate ethical governance frameworks and mandatory curriculum inclusion of AI accountability.	“When an AI generates a concept in seconds, assessing the student’s original contribution and ensuring IP rights are respected becomes a governance nightmare that requires explicit policy.”It is becoming verydifficult for teachers to tell the difference between a student’s own work andwhat the AI created. We need clear rules to make sure students are still learning the core design theories and not just letting the machine do the thinking.When a design is made using millions of existing images, the question of who truly owns the final productis a major legal concern. Our training must include lessons on the ethics of using AI-generated content to avoid copying other people’s creative ideas.	Confirms the centrality of addressing ethical concerns and overhauling outdated curricula.7
5. Future of Work	While entry-level technical execution roles are threatened, AI creates new, high-demand, strategic roles focused on the intersection of design, data, and user experience.	“We need to swiftly pivot our program titles and objectives away from traditional craft roles and toward design system architecture and human-AI collaboration roles.” The curriculum needs to stop treating AI as a shortcut and start teaching it as a primary design partner. The future isn’t Human vs. AI, it’s about the designer who knows how to audit an algorithm’s creative choices.We need to move from teaching ‘how to use the brush’ to teaching ‘how to architect the vision’ that the AI executes. TVET centers must transition from ‘tool-based training’ to ‘system-based thinking’ to keep students employable.	Links TVET outcomes proactively to the digital economy’s demands seven and mitigates job decline fears.

6        Limitations of the Study

Even though the expert interviews were thoroughly analyzed, several weaknesses are associated with the research design, which should be acknowledged. The major limitation is using a purposive and limited sample size (10 expert participants). Whereas such a small sample was logically explained by the principle of saturation of data obtained in a highly specialized field, it constrains the extrapolative potential of statistical data to the general TVET sector in South Asia, and it may limit the ability to reflect the empirical reality of the struggle of the grassroots institutions operating with exceedingly limited resources. Additionally, the analysis was limited to Art and Design TVET programs (fashion, graphic design, textile, and interior design). Consequently, the findings on curriculum and pedagogical change might not be directly applicable to industrial training orders or hard sciences in TVET, where AI implementation (like a virtual laboratory or predictive maintenance) has a much different extent of the issue. Though the coverage of this methodology was as exhaustive as the scope of the study demanded, the attained narratives demanded an interpretative step to be undertaken, and thus, to be careful with the application of findings in the coming empirical field research (Bartholomew Joseph et al. 2025).

7        Recommendations

It is recommended that actionable recommendations to challenge the South Asian policymakers, TVET administrators, and curriculum creators should be the following, based on the synthesis of expert insights on the strategic imperatives, critical barriers, and ethical requirements:

1. Policy and Governance A national and multi-stakeholder strategic model of AI integration in TVET should be established immediately. Such a structure ought to include industry partners, legal practitioners, and TVET management so as to have centralized funding, homogenous moral mandates, and institutional responsibility regarding modernization activities.

2. Curriculum Reform: The institutions should require all Art & Design diplomas to include Critical AI Literacy modules. This set of modules has to go beyond the operation of the current tools and focus on systems knowledge, data provenance, ethical decision-making, and advanced prompt construction, formalizing the essence of the hybrid set of skills. To counter job obsolescence, the curriculum should actively meet the needs of new roles such as UI/UX designer and AI visualizer.

3. Instructor Development: A systematic national Continuous Professional Development (CPD) program devoted particularly to AI-Enabled Pedagogy must be introduced by the governments and educational organizations, which not only covers functional AI skills but teaches instructors how to approach problems of complex ethical evaluation, transversality, and leads students towards a reasonable compromise of traditional and digital methods of creative work.19

4. Assessment Innovation: TVET institutions need to cease and eliminate traditional output-oriented assessments and use competency-based frameworks when assessing the creative process of using AI, overseeing the approach, delivering transparent disclosure and responsibility of the student in the creative process, and upholding ethical soundness.

5. Proactive Skills Forecasting: To be agile to the advancing speed of technology change, TVET will be shifted to providing flexible and stackable micro-credentials in new high-demand roles (e.g., AI Visualization Specialist).1 This will be done through AI in skills forecasting, whereby the training provisions will be agile to respond to the demands of the labor market in real-time and thus maximize the overall usefulness and job placement rates among graduates.

8        Conclusion

The introduction of Artificial Intelligence in Art and Design TVET programs is imperative. It cannot be negotiated as a strategic necessity to ensure the survivability of vocational education in the digital economy. The discussion with the expert views suggests that a genuine integration will not happen without a fundamental, systemic change, but rather a gradual change. Alums will have to switch to being engineers who operate AI systems and critical managers with hybrid skill sets, applying advanced prompting and transversal creative problem-solving skills to the underlying technical expertise. The first institutional obstacles include the pressing necessity of thorough professional training of instructors and the creation of a strong system of ethical governance that could positively address the problem of academic cheating and algorithmic discrimination. Through strategic planning and intervention, ruthless attention to critical literacy, ethical competence, and dynamic adaptation to disruption in the curriculum, the TVET systems in South Asia will overcome the disruptive power of GAI by equipping their graduates with the necessary means of dominating the disruptive forces.

References

Bartholomew Joseph, O., Olateji, M., Matthew Ijiga, O., Okoli, I., & Frempong, D. (2025). Future-Proofing Skills in the Global South: Strategic Directions for Transforming Technical and Vocational Education and Training (TVET). In: International Journal of Advanced Multidisciplinary Research and Studies, 5, 2, 2478–2492. Online: https://doi.org/10.62225/2583049x.2025.5.2.4730 (retrieved 09.03.2026).

Baako, I. (2025). A Bibliometric investigation of Artificial Intelligence in Technical and Vocational Education Training (AI-TVET): Trends and insights for a decade. Research Square (Research Square). Online: https://doi.org/10.21203/rs.3.rs-6431518/v1 (retrieved 09.03.2026).

Okanya, V. (2023). Enhancing Integration of Emerging Technologies in Technical Vocational Education and Training (TVET) Programs for Sustainable Development. In: Industrial Technology Education Research Journal, 6, 1, 73–85. Online: https://iterj.org/pub/article/view/vol-6-no-1-art-7 (retrieved 09.03.2026).

Pirzada, G. (2025). Integration of AI into Art & Design TVET Curricula: Expert Perspectives on Strategies and Implications. TVET Asia.

Muhammad, N., & Abdullah, F. (2024). Exploring the Integration of Artificial Intelligence in Technical and Vocational Education and Training (TVET): Applications, Benefits, Challenges, and Future Prospects. In: Borneo Engineering & Advanced Multidisciplinary International Journal, 3(Special Issue (ICo-ASCNITech 2024)), 57–63. Online: https://beam.pmu.edu.my/index.php/beam/article/view/189 (retrieved 09.03.2026).

El Hayani, M. & Benamar, F. (2025). A conceptual framework for a symbiotic integration of generative AI in post-secondary technical and vocational education and training (TVET): Bridging pedagogy, technology, and ethics. In: International Journal of Accounting, Finance, Auditing, Management and Economics, 6, 9. Online: https://hal.science/hal-05243404v1/document (retrieved 09.03.2026).

MuFan, K. (2023). Applications and challenges of artificial intelligence in the future of art education. In: Pacific International Journal, 6, 3, 61–65. Online: https://doi.org/10.55014/pij.v6i3.405 (retrieved 09.03.2026).

Özer, M. (2024). A roadmap to improve vocational education and training in Türkiye. In: Kapadokya Eğitim Dergisi, 5, 1, 100–111. Online: https://dergipark.org.tr/en/pub/kaped/article/1412082 (retrieved 09.03.2026).

Mohd, B., Kamal, A., & Mohamad. (2025). Integrating AI-Based Moodboard in Fashion TVET Curriculum: A Pilot Study in Digital Ideation Skills. In: Prosiding Seminar Nasional Teknologi Komputer Dan Sains, 3, 1, 324–331. Online:https://seminars.id/prosiding/prosainteks/article/view/369 (retrieved 09.03.2026).

Selvi Rajamanickam, Rus, R. C., Raji, A., Mina, H., & Rian Vebrianto. (2024). Enhancing TVET Education for the Future: A Comprehensive Review of Strategies and Approaches. In: Journal of Advanced Research in Applied Sciences and Engineering Technology, 56, 2, 69–91. Online: https://doi.org/10.37934/araset.56.2.6991 (retrieved 09.03.2026).

Siti Soleha Razali, Ismail, A., Yazid, F. M., Ahmad, M. F., Hashim, S., Reyanhealme Rohanai, & Shafieek, M. (2023). TVET in The 21st Century: Exploring Multimedia Elements in Digital Teaching and Learning Based On Art Content. In: Journal of Technical Education and Training, 15, 1, 9–19. Online: https://publisher.uthm.edu.my/ojs/index.php/JTET/article/view/13511 (retrieved 09.03.2026).

The Role of Artificial Intelligence in Fostering Workplace Integrated Learning at a South African TVET College During Covid-19 – ProQuest. (2023). Proquest.com. Online: https://www.proquest.com/openview/70ecf90df3b593c3cbb1c1b49aea8ff7/1?pq-origsite=gscholar&cbl=2026366&diss=y(retrieved 09.03.2026).

UNESCO. (2022). Artificial intelligence and education: Guidance for policymakers. United Nations Educational, Scientific and Cultural Organization. 

UNESCO-UNEVOC International Centre. (2023). TVET students’ AI competence (K. Nuthall, Ed.). UNESCO. Online: https://unevoc.unesco.org/up/TVET_students_AI_competence.pdf (retrieved 09.03.2026).

World Economic Forum. (2025). The future of jobs report 2025. Online: https://www.weforum.org/publications/the-future-of-jobs-report-2025/ (retrieved 09.03.2026). 

Yaseen, M., & Rahman, F. (2024). AI in vocational and technical education: Revolutionizing skill-based learning. In: EPRA International Journal of Multidisciplinary Research, 10, 5, 180–187. Online: https://doi.org/10.36713/epra20462(retrieved 09.03.2026).

Zhang, S., Chen, Y., Yu, D., Wang, X., & Wu, W. (2024). Artificial intelligence literacy and sustainability of digital learning behaviors in vocational higher education: A COM-B perspective. In: Education Sciences, 14, 4, 384. Online: https://doi.org/10.3390/educsci14040384 (retrieved 09.03.2026).

Abstract

The Fourth Industrial Revolution (IR 4.0) requires innovative teaching approaches in Technical and Vocational Education and Training (TVET). This paper presents the VR Machine Workshop, a digital learning environment designed to introduce measurement concepts and machining fundamentals through an immersive 3D platform. By allowing learners to explore visual representations of precision measurement tools such as vernier callipers, micrometers, and height gauges, the system provides a safe and cost-effective environment for repeated practice while reducing dependency on physical equipment.
The project was piloted with Sijil Kemahiran Malaysia (SKM) Level 3 CNC Machining students using a blended learning approach that combined VR-based training through the Artsteps platform with conventional workshop practice. Results indicate improvements in conceptual understanding, measurement accuracy, and learner confidence, accompanied by higher engagement and sustained learning focus.
At its current stage, the VR Machine Workshop functions as a VR-based instructional environment rather than a fully autonomous artificial intelligence (AI) system. It should therefore be understood as an AI-ready learning approach that demonstrates potential for future integration of intelligent features such as adaptive learning guidance, performance analytics, and personalised learning pathways. The study illustrates how immersive VR environments can support the digital transformation of TVET pedagogy in the Industry 4.0 context.

Keywords: TVET, Virtual Reality, VR Learning Environment, Artificial Intelligence Readiness, CNC Machining

1        Introduction

The Fourth Industrial Revolution (IR 4.0) has significantly reshaped the nature of work and learning, requiring a workforce that is not only technically competent but also digitally adaptive and innovation-oriented. Within the Technical and Vocational Education and Training (TVET) ecosystem, this transformation calls for the integration of emerging technologies that support new approaches to teaching and learning while preparing students for increasingly digitalised industrial environments.

Machining-based programmes remain fundamental to the manufacturing sector; however, training practices in many TVET institutions still rely heavily on physical workshops and conventional equipment. Although such approaches are essential for hands-on skill development, they also present several persistent challenges. These include high maintenance costs, limited access to equipment, safety considerations, and unequal opportunities for repeated practice among students. As the ratio of learners to machines increases, opportunities for mastery-oriented learning and repeated conceptual reinforcement become increasingly constrained.

In response to these challenges, the VR Machine Workshop was developed as an immersive digital learning environment designed to support the introduction of machining measurement concepts through visual and exploratory learning experiences. The environment presents visual representations of precision measurement tools—such as vernier callipers, micrometers, and height gauges—together with supporting instructional materials including explanatory notes, diagrams, demonstration videos, and guided learning prompts. By exploring these materials within a structured 3D environment, learners are able to develop a clearer conceptual understanding of measurement principles before engaging with physical tools in workshop settings.

Beyond its immediate instructional function, the VR Machine Workshop reflects a broader pedagogical shift toward technology-enhanced learning within TVET. Digital learning environments such as VR-based platforms offer opportunities to expand access to instructional resources, support flexible learning pathways, and enhance learner engagement through immersive visualisation. These approaches align with the growing emphasis on digitalisation and technology integration in vocational education, particularly within Industry 4.0-oriented training systems.

While the current implementation focuses on immersive visualisation and structured instructional materials delivered through the VR platform, the VR Machine Workshop also establishes a foundation for future integration of intelligent educational technologies. In particular, the system is conceptualised as an AI-ready learning environment, where future development may incorporate features such as adaptive learning guidance, performance analytics, and personalised learning pathways.

Previous discussions in TVET@Asia have highlighted the increasing importance of digital transformation, simulation technologies, and Industry 4.0-oriented pedagogies in vocational education. Building on this discourse, the present study contributes empirical insights from the Malaysian TVET context by examining how a VR-based learning environment can support measurement tool learning and machining-related knowledge within digitally evolving training systems.

Although the VR Machine Workshop incorporates elements associated with digital learning environments, the current system does not operate as a fully autonomous Artificial Intelligence (AI) platform. Rather, it represents a technology-enhanced learning approach that demonstrates the potential for future integration of intelligent learning features within TVET training environments.

2        Objectives and Scope

The primary objective of the VR Machine Workshop project is to introduce Virtual Reality (VR) as an immersive, learner-centred pedagogical innovation that supports measurement tool learning in technical and engineering education, while establishing a foundation for future AI-enhanced learning environments in TVET. Specifically, the project aims to:

1. Enhance conceptual understanding of measurement principles and tool functions (vernier callipers, micrometers, height gauges, and dial indicators) through immersive 3D learning visualisations that support clearer interpretation of measurement concepts.
2. Provide a safe, sustainable, and cost-efficient digital learning environment that reduces safety risks, material waste, and equipment wear while supporting environmentally responsible training practices.
3. Encourage autonomous and exploratory learning by providing visual guidance, embedded learning prompts, and flexible self-paced review of instructional materials that may later evolve into AI-supported adaptive learning systems.
4. Support Malaysia’s TVET Digitalization Roadmap and Industry 4.0 initiatives by integrating immersive digital learning technologies into skill-based training curricula.
5. Promote inclusive and sustainable digital learning ecosystems that enhance accessibility, equity, and continuous innovation in the transformation of TVET pedagogy.

The project targets TVET students, technical instructors, industrial apprentices, and lifelong learners in machining-related disciplines. Its modular design enables integration into blended learning environments and provides potential for future expansion into other technical domains such as welding, automotive, and mechatronics.

3        Integration of Industry 4.0 and AI-Driven Technologies

The integration of Industry 4.0 technologies within the VR Machine Workshop aims to modernise technical training approaches in the Technical and Vocational Education and Training (TVET) ecosystem. The platform utilises Virtual Reality (VR) to provide an immersive digital learning environment that supports the teaching and learning of machining measurement concepts.

The VR Machine Workshop was developed using the Artsteps platform, which enables the creation of immersive 3D learning spaces. Within this VR learning environment, instructional materials related to precision measurement tools such as vernier callipers, micrometers, and height gauges are presented through visual explanations, learning notes, and guided information panels. Through this environment, learners are able to explore the displayed tools, access instructional materials, and develop a clearer understanding of measurement concepts before engaging with physical equipment in the actual machining workshop.

This approach supports experiential and self-directed learning, allowing students to revisit learning materials and explore the environment at their own pace. By providing visualisation and contextual explanations within a VR-based learning environment, the VR Machine Workshop helps learners build conceptual understanding while reducing dependency on limited physical workshop resources.

At the current stage, the VR Machine Workshop does not function as a fully autonomous Artificial Intelligence (AI) system. The learning environment primarily delivers structured instructional content through the VR platform rather than automated AI-driven decision-making processes. Therefore, the system should be understood as a technology-enhanced learning environment that utilises VR for instructional delivery.

Nevertheless, the VR Machine Workshop represents an initial step toward the future integration of intelligent technologies in TVET training. With further development, VR-based learning environments such as this may incorporate AI-supported features including adaptive learning guidance, performance analytics, and personalised learning pathways. Such developments could further enhance the effectiveness of immersive learning environments within Industry 4.0-oriented TVET education.

4        Methodology

To ensure that the VR Machine Workshop was developed with practical relevance and pedagogical integrity, a user-centred design approach was adopted. This methodology emphasised the active involvement of end users—particularly TVET learners, instructors, and technical practitioners—throughout every stage of the design and development process. By engaging actual users, the project ensured that the VR learning environment reflects authentic machining contexts while addressing real training challenges encountered in technical education.

The development process consisted of several key phases, including needs analysis, VR learning environment design, content development, and usability evaluation. The needs analysis phase involved consultations with machining instructors and students to identify critical skills and measurement procedures that often pose difficulties during conventional workshop sessions. Based on these insights, a VR learning environment was developed using the Artsteps platform, which enables the creation of immersive 3D spaces where visual representations of measurement tools and instructional materials can be organised in an accessible learning format.

The learning design followed a progressive sequence that began with the introduction of theoretical concepts, continued with guided observation of measurement tools and demonstrations, and culminated in self-directed exploration within the VR learning environment. This structure allows learners to review learning materials repeatedly, strengthening their conceptual understanding before performing measurements using physical instruments in the workshop.

Learning effectiveness was evaluated using a pre- and post-test design. Learning gains were analysed through descriptive comparison, with percentage improvement calculated to assess changes in learners’ conceptual understanding, measurement accuracy, and CNC machining readiness following the VR-based learning intervention.

A pilot implementation was conducted with a group of machining students and instructors to assess the system’s usability, learning effectiveness, and user experience. Feedback collected from participants was used to refine interface design, instructional clarity, and navigation flow, ensuring the innovation remains accessible, relevant, and scalable for broader adoption across TVET institutions.

4.1       Development Platform and Tools

The VR learning environment was developed using Artsteps, an online 3D immersive environment builder that enables the creation of virtual exhibition-style spaces accessible through both desktop and VR interfaces. Artsteps was selected for its accessibility, low technical barrier, and compatibility with multiple devices—including VR headsets, laptops, and mobile browsers—making it suitable for implementation within Malaysian TVET institutions with varying levels of technological infrastructure.

Within Artsteps, the development process involved creating a VR learning space that presents visual references to precision measurement tools and related instructional materials. The digital environment was organised to reflect the layout and context of machining-related equipment, allowing learners to become familiar with the appearance and functions of various measurement instruments. Key tools presented in the environment included vernier callipers, micrometers, height gauges, dial indicators, and surface plates.

Instructional materials were embedded within the environment using Artsteps’ information panels, hyperlinks, and multimedia display features. These elements allow learning content—such as explanatory notes, tool descriptions, demonstration videos, and supporting diagrams—to be presented alongside the visual representations of the tools. This approach enables learners to explore the environment while accessing structured learning materials related to measurement concepts and tool usage.

Learners navigate the environment through a first-person perspective using keyboard and mouse controls or VR-compatible devices. This spatial exploration allows students to move within the VR learning space, observe tools from different viewpoints, and access contextual learning materials provided through interactive information points.

The pedagogical design of the Artsteps environment follows a progressive scaffolding approach, beginning with conceptual explanations of measurement principles, followed by guided observation of tool demonstrations, and concluding with self-directed review of learning materials within the VR learning environment.

Table 1: Learning phases for measurement tool integration in the VR Machine Workshop

Learning Phase	Description	Tool Integration
Concept Introduction	Learners explore measurement principles and the basic structure of tools through visual explanations and descriptive learning panels within the VR environment.	Information panels, 3D visual references
Guided Observation	Learners observe demonstrations of measurement tool usage through embedded videos, diagrams, and instructional materials.	Video demonstrations, multimedia displays
Self-Assessment	Learners answer embedded questions and reflection prompts while exploring the VR learning environment to reinforce understanding of measurement concepts and tool identification.	Interactive hotspots, linked question prompts

4.2       Workflow Design and Development Stages

The development of the VR Machine Workshop followed a structured instructional design workflow adapted from the widely recognised ADDIE framework—Analysis, Design, Development, Implementation, and Evaluation. This systematic process ensured that the innovation was pedagogically grounded and aligned with the practical learning needs of TVET students and instructors.

During the Analysis phase, consultations were conducted with machining instructors and students to identify common learning challenges related to measurement tools and machining practices. Classroom observations revealed several constraints in conventional workshop training, including limited access to precision instruments, safety considerations, and restricted opportunities for repeated conceptual explanation during practical sessions. Based on these findings, the learning objectives were aligned with the National Occupational Skills Standard (NOSS) for CNC Machining Level 3.

The Design phase focused on organising the structure of the VR learning environment and arranging the instructional materials in a logical learning sequence. The environment was designed to visually introduce measurement tools and their functions through explanatory panels, diagrams, and demonstration media. The goal of this phase was to create a clear learning flow that supports conceptual understanding before students engage with physical tools in workshop-based training.

In the Development phase, the VR learning environment was created using the Artsteps platform. Visual representations of measurement tools and related instructional materials were organised within the digital space using information panels, multimedia content, and linked learning resources. These components allow learners to explore the environment while accessing explanations, demonstrations, and guided learning prompts related to measurement concepts.

The Implementation phase involved introducing the VR learning environment to a group of 30 Sijil Kemahiran Malaysia (SKM) Level 3 CNC Machining students over a four-week training period. Students explored the environment as a preparatory learning activity before participating in physical workshop sessions. Instructors facilitated the sessions by guiding learners through the instructional materials and encouraging self-directed exploration of the digital environment.

Finally, the Evaluation phase employed a mixed-method research design that combined quantitative and qualitative data collection to examine the effectiveness of the innovation. Quantitative data were obtained through pre- and post-tests that measured conceptual understanding, measurement accuracy, and tool identification skills. Complementary qualitative data were collected through interviews, reflective journals, and observation checklists to capture learners’ engagement, perceptions, and overall learning experiences.

4.3       Data Collection and Analysis

To evaluate the effectiveness of the VR Machine Workshop, a pre- and post-test design was employed. The assessment involved 30 Sijil Kemahiran Malaysia (SKM) Level 3 CNC Machining students who participated in the VR-based learning activities during the implementation phase.

The pre-test was administered prior to students’ exposure to the VR learning environment in order to establish a baseline of their conceptual understanding of measurement tools and related machining principles. Following the learning sessions, a post-test was conducted to measure changes in students’ understanding, tool identification skills, and measurement-related knowledge.

Quantitative data were analysed using descriptive statistical comparison between the pre-test and post-test results. Mean score differences and percentage improvements were calculated to determine the extent of learning gains following the VR-based learning intervention.

In addition to the quantitative assessment, qualitative data were collected through observation notes, learner reflections, and informal feedback discussions with instructors. These qualitative insights provided additional perspectives on learner engagement, perceived usefulness of the VR learning environment, and overall learning experiences.

Given that the study involved a relatively small sample drawn from a single training cohort, the results should be interpreted as preliminary findings within a pilot implementation context. Future studies may involve larger participant groups and longer implementation periods to further validate the effectiveness of VR-supported learning environments in TVET education.

4.3.1      Research Design

The quantitative component measured improvements in learner performance through pre- and post-tests administered before and after the VR training module. The tests assessed three key aspects: understanding of measurement theory, accuracy of tool readings, and ability to identify and handle precision instruments correctly. Comparison of the pre- and post-test scores provided measurable evidence of learning improvement resulting from the VR-based training.

The qualitative component complemented these findings by exploring learners’ experiences, perceptions, and engagement with the VR system. Data were collected through semi-structured interviews, student reflection journals, and observation checklists during the training sessions. The qualitative inquiry focused on dimensions such as motivation, usability, immersion, and perceived learning value of the virtual environment.

4.3.2      Sample and Procedure

The study sample consisted of 30 Sijil Kemahiran Malaysia (SKM) Level 3 CNC Machining students who participated in a four-week training programme. All participants completed the pre-test prior to engaging with the VR module and the post-test upon completion. During implementation, instructors served as facilitators and observers, recording patterns of engagement, common errors, and behavioural changes in learning. Interviews and reflections were collected immediately after the final session to capture fresh and authentic feedback from participants.  

All procedures and data collection activities were conducted in accordance with institutional guidelines and administrative approval from ADTEC JTM Kampus Kuantan. Participant involvement was voluntary and anonymised for reporting purposes.

4.3.3      Analysis Methods

Quantitative data were analysed using descriptive statistical comparison between the pre- and post-test scores obtained from the 30 participating students. Mean score differences and percentage improvement were calculated to determine the effectiveness of the VR Machine Workshop intervention in enhancing learners’ conceptual understanding and measurement accuracy.

As the study involved a relatively small sample drawn from a single training cohort, the findings should be interpreted as preliminary evidence within the pilot implementation context.

Qualitative data were analysed using thematic analysis through an inductive approach. Interview transcripts, reflection journals, and observation notes were coded to identify recurring themes such as learning motivation, self-confidence, ease of use, and perceived usefulness of the embedded learning prompts and instructional materials. Triangulation of both quantitative and qualitative findings provided a holistic view of learner performance (objective outcomes) and user experience (subjective engagement).

4.3.4      Reliability and Validity

To ensure data reliability and validity, all research instruments—including test items, observation sheets, and interview protocols—were reviewed by three subject matter experts in machining and TVET pedagogy. The pre- and post-tests contained identical items presented in different sequences to minimise recall bias. Additionally, interviews were conducted by independent researchers not directly involved in instruction to avoid subjective influence.

4.3.5      Preliminary Findings

Quantitative analysis revealed an overall 27% improvement in post-test scores, indicating a substantial increase in learners’ conceptual understanding and measurement proficiency. These findings are consistent with the quantitative results presented in Table 2. Qualitative feedback further supported these outcomes, with participants reporting that the VR-based training provided a more engaging, comprehensible, and safe learning experience compared to traditional workshop practice. Learners consistently identified the guided learning prompts and instructional materials as the most helpful features, as they supported clearer understanding of measurement concepts.

5        Findings and Discussions

The evaluation of the VR Machine Workshop produced substantial evidence of its educational, technical, and institutional impact within the Malaysian TVET context. The integration of both quantitative and qualitative data provided a holistic understanding of how immersive VR learning environments can enhance teaching effectiveness and learning experiences in machining-based training.

5.1       Quantitative Findings: Learning Gains and Performance Outcomes

Pre- and post-test analyses revealed notable improvements in learners’ comprehension and measurement accuracy following the VR Machine Workshop intervention. Across the cohort of 30 trainees, post-test scores improved by an overall average of 27%, with the most prominent gains observed in reading precision, error detection, and appropriate tool identification

Table 2: Pre- and Post-Test Learning Outcomes Following VR Machine Workshop Intervention

Assessment Area	Pre-Test Mean Score (%)	Post-Test Mean Score (%)	Improvement (%)
Measurement tool selection (steel rule, vernier calliper, micrometer)	58	82	+24
Measurement accuracy and reading interpretation	61	86	+25
Thread identification and pitch measurement	55	83	+28
Alignment awareness using Dial Test Indicator (DTI)	57	84	+27
CNC machining readiness (milling & lathe concepts)	60	87	+27
Overall mean learning gain	58	85	+27

As shown in Table 2, learners demonstrated faster recognition of measurement values and higher consistency in repeat measurements after VR-based practice. These findings suggest that immersive VR learning environments can help bridge the gap between conceptual understanding and practical workshop application.

By exploring visual representations of measurement tools prior to entering the physical workshop, learners developed clearer mental models that translated effectively into real-world performance. As one instructor noted, “When they finally entered the real workshop, I didn’t have to repeat the basic demonstrations — they already knew how to hold and read the tools correctly.”

The quantitative improvements also align with constructivist learning theory, which emphasises active engagement and reflection in knowledge construction (Billet 2001). The system’s learning prompts and instructional explanations helped reinforce correct measurement interpretation, reinforcing procedural accuracy and fostering learner independence.

5.2       Qualitative Insights: Learner Engagement, Confidence, and Motivation

The qualitative data — derived from semi-structured interviews, reflection journals, and instructor observations — revealed deeper insights into learners’ emotional and cognitive engagement. Thematic coding identified four recurring themes: motivation, confidence, autonomy, and feedback-driven learning.

Learners consistently expressed that the VR Machine Workshop made complex measurement concepts more approachable and less intimidating. Many described the virtual environment as “safe,” “realistic,” and “fun to use.” One participant reflected,

“In the VR workshop, I can practice until I get it right. I don’t have to worry about breaking anything or using up materials.”

This sense of psychological safety was pivotal in building confidence among beginners, especially those who were hesitant to use real tools. Another trainee shared,

“It felt like being in the real lab, but I could stop, replay, and try again anytime. That helped me understand measurement much better before I touched the real tool.”

The freedom to learn through trial and error encouraged intrinsic motivation, aligning with self-determination theory, where autonomy and competence are key to sustained engagement.

From the instructors’ perspective, the change was equally evident. One instructor commented,

“Students came to the physical lab better prepared. I spent less time explaining the basics and more time refining their techniques.”

This observation underscores how the innovation improved teaching efficiency and reshaped classroom dynamics, enabling instructors to shift from repetitive demonstration toward higher-level coaching and mentoring.

Overall, both learners and educators agreed that the VR workshop created a more interactive, less intimidating, and more efficient learning culture, turning what was once a procedural exercise into a dynamic and enjoyable experience.

5.3       Institutional and Pedagogical Impact

The innovation extended its benefits beyond the individual learner level to significantly influence institutional practice and pedagogical design. Instructors reported that the system streamlined their teaching workflows, reducing dependency on physical equipment for basic skill familiarization. This not only minimised scheduling conflicts but also optimized workshop utilization.

One department head noted during feedback discussion,

“Previously, students had to queue for each tool. Now, they complete VR training first — so when they enter the real lab, they use time more efficiently.”

Instructor observations and learner feedback also provided useful insights for improving lesson planning and instructional delivery. Instructors could monitor completion rates, identify common error trends, and adjust lesson plans accordingly. This marks a shift toward data-driven pedagogy, aligning with the global trend of integrating analytics into vocational education (UNESCO-UNEVOC 2022).

From an operational perspective, the system contributed to institutional sustainability by reducing material waste, equipment wear, and energy consumption. The digital format allowed asynchronous learning, giving students flexibility to practice outside formal class hours. These advantages align closely with Malaysia’s TVET Digitalization Roadmap 2030, emphasising sustainability, inclusivity, and innovation in skill delivery.

5.4       Discussion: Educational Implications and Broader Significance

The findings demonstrate that the VR Machine Workshop contributes to bridging theoretical understanding and practical workshop learning through an immersive visual learning approach. The use of Virtual Reality (VR) as a digital learning medium supports improved conceptual understanding of measurement tools and machining principles while enhancing learner engagement. These findings are consistent with recent studies that highlight the educational potential of VR-based environments in vocational education contexts (Long et al. 2024; Thomann 2024).

The recorded increase of 27 percentage points in the overall score provides quantitative evidence that the VR learning environment can strengthen learners’ conceptual understanding of measurement principles prior to hands-on workshop activities. By allowing learners to explore visual representations of measurement tools alongside explanatory materials and demonstrations, the system helps learners become more familiar with tool functions and measurement concepts before engaging with physical instruments in workshop settings. This outcome aligns with experiential learning theory, which emphasises active engagement, visualisation, and repeated exposure to learning materials as important mechanisms for effective skill development (Kolb 2015).

From a broader TVET perspective, the VR Machine Workshop addresses several long-standing constraints in workshop-based training by providing an accessible digital learning resource that complements physical instruction. The environment enables students to review learning materials, observe measurement tools, and reinforce conceptual knowledge before participating in practical workshop activities. These findings support recent literature that identifies immersive technologies as promising tools for enhancing instructional effectiveness and learner engagement in vocational education, particularly within digitally evolving training systems (Muskhir et al. 2024).

Although the study adopts an AI-ready conceptual framing, the reported findings primarily reflect the effectiveness of immersive VR learning supported by structured instructional materials and guided learning prompts rather than autonomous AI-driven processes. Nevertheless, the platform establishes a potential foundation for future integration of intelligent learning technologies in TVET environments, such as adaptive learning guidance and personalised learning support (Abu Bakar et al. 2024). The study therefore positions VR-based learning environments as a practical pathway for enhancing instructional quality and supporting workforce readiness in machining-related TVET education.

6        Conclusion and Future Work

This study demonstrates that the VR Machine Workshop provides a useful digital learning approach for supporting machining education within the TVET context. By presenting measurement tools and related instructional materials within a VR learning environment, the system helps learners develop clearer conceptual understanding before engaging with physical instruments in workshop sessions. The pre-test and post-test comparison showed an overall learning improvement of 27%, indicating that the VR-based learning materials contributed positively to students’ understanding of measurement concepts.

Although the project is conceptually positioned as an AI-ready learning environment, the current implementation primarily delivers structured instructional materials through the VR platform rather than relying on autonomous AI-driven processes. Nevertheless, the platform provides a foundation for potential future development, where intelligent learning features such as adaptive learning guidance or personalised learning support may be explored in subsequent studies.

From a broader perspective, the VR Machine Workshop illustrates how digital learning environments can complement conventional workshop-based training in TVET institutions. By providing accessible visual learning materials and opportunities for self-paced exploration, the system can support students in preparing for practical workshop activities while reducing reliance on limited physical resources.

However, several limitations should be acknowledged. The study was conducted as a pilot implementation involving a relatively small sample of 30 SKM Level 3 CNC Machining students from a single training institution over a limited training period. As such, the findings should be interpreted as preliminary evidence of the effectiveness of the VR Machine Workshop within this specific training context. Future research may involve larger and more diverse participant groups, longer implementation periods, and cross-institutional evaluations to further examine the effectiveness and scalability of VR-supported learning in TVET education.

7        Acknowledgment

The author extends sincere appreciation to the Department of Manufacturing Machining of ADTEC JTM Campus Kuantan for their continuous support, collaboration, and technical expertise during the development of the VR Machine Workshop. Deep gratitude is also conveyed to the Manufacturing Machining students, whose active participation and enthusiasm contributed significantly to the project’s validation. This project contributes to ongoing efforts in advancing digital innovation within Malaysia’s TVET ecosystem.

References

Abu Bakar, M. H., Md Ghafar, N. H., & Abdullah, F. (2024). Exploring the integration of artificial intelligence in technical and vocational education and training (TVET): applications, benefits, challenges, and future prospects. In: Politeknik Mukah Journal, 3(Special Issue), 57–63.

Billett, S. (2001). Learning in the Workplace: Strategies for Effective Practice. St Leonards: Allen & Unwin.

Editorial Issue 21 (2023). The role of excellence in TVET and emerging megatrends such as digitalisation and artificial intelligence. In: TVET@Asia, Issue 21.

Editorial Issue 24 (2025). Vocational didactics I: Construction technology, wood technology and color technology and interior design. In: TVET@Asia, Issue 24.

Irsyad, I. (2025). Economic benefits of technological innovation in education, with focus on AI in TVET. In: Oman Journal of Technology and Progress, 10, 1, 01–07.

Kolb, D. A. (2015). Experiential Learning: Experience as the Source of Learning and Development (2nd ed.). Upper Saddle River, NJ: Pearson Education.

Kuckartz, U. (2014). Qualitative Text Analysis: A Guide to Methods, Practice and Using Software. London: SAGE Publications.

Long, Y., Zhang, X., & Zeng, X. (2024). Application and effect analysis of virtual reality technology in vocational education practical training. In: Education and Information Technologies, 30, 9755–9786.

Ministry of Human Resources Malaysia. (2023). TVET Digitalization Roadmap 2030. Putrajaya: Department of Skills Development (DSD), Ministry of Human Resources Malaysia.

Muskhir, M., Luthfi, A., Watrianthos, R., Usmeldi & Samala, A. D. (2024). Emerging research on virtual reality applications in vocational education: A bibliometric analysis. In: Journal of Information Technology Education: Innovations in Practice, 23, 1-22.

Thomann, H. (2024). How effective is immersive virtual reality for vocational education? In: Computers & Education.

TVET@Asia (2025). Call for Papers: The Impact of Artificial Intelligence (AI) on Technical and Vocational Education and Training Systems and Teaching Practices. In: TVET@Asia, Issue 26.

UNESCO (2023). AI and Education: Guidance for Policy-Makers (2nd ed.). Paris: UNESCO Publishing. Online: https://doi.org/10.54675/PCSP7350 (retrieved 13.03.2026).

UNESCO-UNEVOC. (2022). Digitalization in TVET: The Global Context. Bonn: UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training.

Weigel, T., Mulder, M., & Collins, K. (2007). The concept of competence in the development of vocational education and training in selected EU member states. In: Journal of Vocational Education & Training, 59, 1, 53–66. Online: https://doi.org/10.1080/13636820601145630 (retrieved 13.03.2026).

Zainuddin, Z., Shujahat, M., Haruna, H., & Chu, S. K. W. (2020). The role of gamified e-learning platforms in enhancing motivation and engagement: A systematic review. In: Computers & Education, 160, 104034. Online: https://doi.org/10.1016/j.compedu.2020.104034 (retrieved 13.03.2026).

The health sector is facing many challenges of which digital transformation of work, equality and equity of care, new types of pandemics as well as patients’ rights and involvement in their care are just a few. On the other hand, rapid technological developments and new forms of treatments are opening up new opportunities for the health of patients and populations. The changes and challenges also affect pedagogical and didactical practices of technical and vocational education and training of professionals and their commitment to continuous learning throughout their careers. 

This issue of TVET@Asia gathers original research, case studies, and theoretical perspectives that shed light on the challenges, opportunities, and best practices in health care education including patient-centred education and health promotion. The issue takes a broad approach to the subject in relation to education of professionals, focusing on innovative approaches, evidence-based strategies, and emerging trends that enhance teaching, learning, and practical training in the healthcare field. 

In her case study on Vietnam, CHIEU LINH THI DO (Ho Chi Minh City Vocational College) investigates the social-emotional development for vocational school students through teaching soft skills. Social-emotional learning (SEL) has emerged as a vital component in preparing vocational students for both academic success and mental well-being. In Vietnam, while soft skills are part of the vocational curriculum, the integration of SEL into these programs remains underexplored. This study investigates the social-emotional skill levels among vocational students and examines how soft skills education can foster SEL development. This is done through a quantitative survey with 185 students from technical schools and technical colleges in Ho Chi Minh City, using a structured questionnaire based on the CASEL framework encompassing five core competencies: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. The findings highlight several challenges faced by vocational students, which underlines the importance of embedding SEL more explicitly within soft skills curricula. The study concludes with practical recommendations for educational administrators and teachers. 

The study by JONAS WINZEN, ANNABELL ALBERTZ, & MATTHIAS PILZ (Chair of Business Education and International VET Research, University of Cologne) compares nursing training in terms of health promotion between Indian and German curricula. Thereby, the scope of the analysis is limited to the health promotion of trainee nurses and explicitly not to patients. To address this, the current curricula in nursing training in India and Germany are examined using a content analysis. In addition, three interviews with Indian nurses are conducted to complement the curriculum analysis and to validate implementation in the classroom. The results show that the German curriculum covers the topic of trainees’ health promotion both quantitatively more frequently and qualitatively in greater depth. The Indian curriculum focuses more on general concepts and emphasises the role of nurses as productive members of society. Also, nutrition is treated in more detail in the curriculum in India than in Germany. The results are both innovative, as no detailed studies exist to date, and of practical importance, as they can be used to target recognition processes and any necessary post-qualification activities in the context of the migration of nurses from India to Germany.

In his paper, ENOCK MUSAU (Institute of Transport and Logistics Studies-Africa, University of Johannesburg & Department of Management Science, Kisii University) discusses the promotion of sustainable healthcare transport in health education in Sub-Saharan Africa. The extent to which green mobility concepts are integrated into healthcare education remains largely unexplored so far. This study conducts a bibliometric analysis to map the intellectual landscape at the intersection of sustainable healthcare transport and health education. Drawing from Scopus and Web of Science databases (1970–2025), the analysis utilizes VOSviewer and the Bibliometrix R package to examine co-authorship networks, publication trends, thematic clusters, and keyword co-occurrences. Results indicate growing interest in electric medical vehicles, telemedicine-enabled transport, and low-carbon healthcare logistics. Despite these advancements, sustainability remains marginal in healthcare curricula. This study highlights a significant pedagogical gap and calls for integrating climate-smart transport knowledge into professional training. The findings offer practical insights for educators, curriculum developers, and policymakers aiming to align health education with global sustainability and climate resilience goals. 

The paper by PHILIPP STRUCK (Catholic University of Applied Sciences, Mainz) investigates perspective of apprentices entering nursing education in the German TVET system and its consequences for the recruitment of professionals. In many countries, the educational pathway into nursing is an academic study program. While in Germany, it is also possible to study nursing, the path via TVET is much more frequently chosen. For this study, 15 first-year apprentices were interviewed about their career choice criteria and their future prospects. The interview results indicate that personal motivation or individual reasons for entering the nursing profession are the strongest incentives for pursuing nursing education. Career orientation and internships, as well as biographical experiences and private environment, also played an important role in the career choice. The apprentices cite various factors that could increase their chances of remaining in the nursing profession in the long term. Responses to career choice criteria questions were examined and classified using established theories. The results are discussed in terms of possible consequences and implications for TVET teachers and instructors, with the aim of understanding the perspectives of recruiters and exploring ways to enhance and promote the appeal of nursing education in the German TVET system. 

In their paper, ROSZIATI IBRAHIM, KHADIJAH MD ARIFFIN, MAZIDAH MD REJAB, SAPIEE JAMEL & ABDUL RASID ABDUL RAZZAQ (Universiti Tun Hussein Onn Malaysia, Malaysia) summarize their findings regarding career advancement for students undertaking TVET matriculation program after high school in Malaysia. TVET has long been the main pillar in producing highly skilled human capital in technical and vocational fields in Malaysia. In 2023, TVET matriculation curriculum has been introduced in Malaysian education to expand for meeting the TVET demands in the country. However, concerns persist regarding its effectiveness in preparing the students for their career advancement. This paper summarizes the findings based on the survey instrument that are distributed to the first batch of the students who take the TVET matriculation program. The results from the analysis show that career opportunities are the main factor driving students to choose this program, with 49% of respondents emphasizing this factor. From the academic aspect, 75.2% of the students have a background in pure science during secondary school, indicating an interest and inclination towards technical disciplines. Meanwhile, 40.9% of students stated interest as the main factor influencing their choice. These results show that TVET continues to be a relevant choice among students who want to build a strong career.

This paper refers to vocational education and training in general terms and is not strictly linked to the CfP.

The Editors of Issue 25: 

Marianne Teräs, Suci Tuty Putri, Chee Sern Lai, & Junmin Li

Abstract

The study compares nursing training in terms of health promotion between Indian and German curricula. Therefore, the scope of the analysis is limited to the health promotion of trainee nurses, and it is explicitly not intended for patients. To address this, the current curricula in nursing training in India and Germany are examined using content analysis. In addition, three interviews with Indian nurses were conducted to complement the curriculum analysis and to validate implementation in the classroom. The results show that the German curriculum covers the topic of trainees’ health promotion more frequently and in greater depth, both quantitatively and qualitatively. The Indian curriculum focuses more on general concepts and emphasises the role of nurses as productive members of society. Furthermore, nutrition is treated in more detail in the curriculum in India than in Germany. The results are both innovative, as no detailed studies exist to date, and of practical importance, as they can be used to target recognition processes and any necessary post-qualification activities in the context of nurse migration from India to Germany.

Keywords: Nurses, India, Germany, Health Promotion, Curriculum

1        Introduction

To address the increasing demand for skilled healthcare professionals in Germany (Heinen et al. 2013), the recruitment of nurses from abroad has intensified in recent years (Reiff et al. 2020), including those from India (Goel 2013; Datta & Basu 2023). Indian nurses are in high demand in the global workforce, as well as in Germany, due to their well-respected training and professional reputation (Walton-Roberts & Irudaya Rajan 2023). The migration of nurses from India to Germany has a long-standing tradition and continuing connection, originating in the 1960s and 1970s through initiatives by Catholic networks and church-affiliated organisations (Wichterich 2024). However, while studies have examined the migration of Indian nurses, including the factors influencing their migration (e.g., Oda et al. 2018), there is a lack of research specifically addressing and comparing the professional training of nurses in India with that in Germany. In view of the extensive Indo-German project activities initiated in recent years, the high number of participants, and the pressing societal need in light of the nursing crisis and workforce shortages in Germany (Wittenborg & Wollnik 2014; von Ungern-Sternberg 2025), a knowledge gap exists in this area. 

Therefore, this study aims to compare nursing training in India and Germany, with a particular focus on the integration of health promotion in training programmes. The overarching objective is to explore various approaches to health promotion within nursing education and to identify any potential particularities. The research question can therefore be formulated as follows: How is health promotion incorporated within the curricula of German and Indian nursing training, and in what ways do they differ? The findings can contribute to improving the recognition of Indian nursing qualifications in Germany by providing substantive evidence and, where necessary, identifying gaps that require targeted qualification measures (Biebeler et al. 2016; Wichterich 2024). 

A crucial aspect of this study is its focus on health promotion for nursing trainees, rather than for patients. The increasing workload and stress levels faced by nurses worldwide make this specific emphasis highly relevant (Fiabane et al. 2013; Magnavita 2014). Research has shown that nurses are particularly susceptible to occupational stress due to the demands of their profession (Wesselborg & Bauknecht 2025). Physical complaints, psychological exhaustion and high dropout rates have been identified as potential consequences. Given that nurses experience significant physical and mental strain, health promotion plays a crucial role in their professional development (Babapour et al. 2022). Within the fields of nursing training, the structural and curricular framework conditions established serve as the fundamental basis for a conducive approach to professional challenges. The knowledge and competencies held by nurses in regard to health promotion are of crucial relevance, given their capacity to exert a beneficial influence on nurses’ perceptions and practices (Melariri et al. 2022). 

To compare the integration of health promotion in nursing training in India and Germany, the respective curricula were analysed. Curriculum studies facilitate the analysis of content-related, structural, and normative aspects, as demonstrated by Dukpa and Pattanaik (2025) in their research on vocational training curricula in India’s construction sector and by Chinengundu (2025) on the South African construction technology curriculum. In the present study, health promotion and related specific aspects served as the comparison parameter between Indian and German curricula, also referred to as tertium comparationis (Pilz 2012). The application of a clearly defined, country-independent criterion of comparison facilitates the systematic examination of similarities and differences between countries. In addition to the curriculum analysis, interviews were conducted with Indian nurses to validate the content and practical implementation of the Indian curriculum. The interviews were conducted exclusively in India because, unlike the German context (Jakobs & Vogler 2020; Wochnik et al. 2022; Großmann et al. 2023; Olden et al. 2023), no empirical findings exist regarding the detailed implementation of the nursing curriculum in the classroom. Therefore, the interviews aimed to bridge the gap between the curriculum’s formal content and its actual relevance in training practice in the Indian context. This validation is necessary for India, as there is a general lack of detailed information on how curricula are implemented in vocational and professional training (Zenner et al. 2017; Schneider et al. 2023). Moreover, insights from Indian nurses provided essential guidance for accurately interpreting the Indian curriculum, which may be unfamiliar to German researchers.

The next chapter provides a brief conceptual and empirical introduction to health promotion, followed by an overview of nursing training in India and Germany. The methodology is outlined in Chapter Four, followed by the results of the curriculum analysis and the interviews. Finally, the results are discussed, and a concluding section presents potential future research directions.

2        The concept of health promotion

Health promotion has evolved into a central concept in healthcare and health sciences over the past decades. The term “health promotion” extends beyond the mere prevention of diseases and encompasses measures aimed at actively strengthening and maintaining individual and community health. The WHO (1986) describes health promotion as “the process of enabling people to increase control over, and to improve, their health. To reach a state of complete physical, mental and social well-being, an individual or group must be able to identify and to realise aspirations, to satisfy needs, and to change or cope with the environment”. Individuals should be empowered to make informed decisions about their health. This requires the dissemination of knowledge and skills, as well as access to health resources. Furthermore, a health-promoting society relies on the active participation of those affected. Individuals and communities must be included in decision-making processes to ensure that measures are tailored to their needs. Additionally, environments must be created that facilitate healthy choices (WHO 1986).

Health-promoting behaviour encompasses physical activity, health responsibility, interpersonal relations, nutrition, spiritual growth, and stress management (Walker et al. 1987). Thus, health promotion can be categorised into physical and psychological/mental dimensions. The physical dimension encompasses measures aimed at maintaining physical health, including regular physical activity and a balanced diet. In the workplace, preventing occupational diseases and promoting a body-conscious work style are particularly relevant. The psychological dimension of health promotion focuses on mental well-being. This includes strategies for stress management, resilience enhancement, burnout prevention, and fostering a positive self-concept. Given the rising prevalence of mental health disorders in professional settings, particularly in high-stress occupations such as nursing, this dimension is gaining increasing importance (Wesselborg & Bauknecht 2025). As demonstrated in the literature reviews by Schaller et al. (2022) and Proper and van Oostrom (2019), the majority of studies focusing on health promotion primarily seek to enhance the mental well-being of nurses, with a particular emphasis on stress reduction (Stanulewicz et al. 2020). However, it is crucial to recognise the inherent interconnectedness between physical and psychological dimensions. Psychological stress has been shown to have a detrimental effect on physical health, and conversely, physical health concerns can adversely impact mental well-being. Otto et al. (2019) reveal that nurses surveyed experience chronic stress and physical strain, highlighting the necessity for ergonomic and strength training, as well as stress management strategies. Additionally, there is a requirement for holistic nursing interventions, such as educational classes, massage therapy and employee wellness programmes (McElligott et al. 2009). In this context, Mojtahedzadeh et al. (2022) emphasise in their study that health promotion measures already exist, such as exercise programmes, subsidies for fitness studios and back training. Nevertheless, the responding nursing staff complained that they often lack the energy to take advantage of these programmes after work and that programmes should be held during working hours instead. 

In addition to the existing literature on the measures employed by institutions and employers to promote the physical and mental health of nurses, there is also research focusing on nurses, particularly nursing trainees, and their behaviour regarding health promotion. For instance, both Mak et al. (2018) and Alzahrani et al. (2019) found that nursing students exhibited a high level of engagement in interpersonal relationships and spiritual growth, but a low level of physical activity. The significant factors, such as health responsibility, spiritual growth, stress management and physical activity as predictors of health promotion, underscore the necessity for curriculum adjustments. Consequently, Mooney et al. (2011) and Alpar et al. (2008) underline that health promotion should be a constant and integral part of training, as it facilitates the development of a more profound understanding among trainees, complemented by practical experience. This notion is further substantiated by Whitehead’s (2007) literature review. As a result, the present study is dedicated to the curricular integration of health promotion in nursing training programmes in India and Germany.

3        Nursing training in India and Germany

3.1       India

The Indian education system is one of the largest in the world, reflecting the country’s social, cultural, and economic realities. A distinctive feature of the Indian education system is its strong emphasis on academic education, even in fields that are typically taught in a more vocational or practice-oriented manner in other countries (Wessels & Pilz 2018; Pilz & Regel 2021; Schneider & Pilz 2024).

The Indian Nursing Council recognises three distinct foundational nursing education programmes: Auxiliary Nursing & Midwifery (ANM), General Nursing & Midwifery (GNM) and the Bachelor of Science in Nursing (B.Sc. Nursing). The ANM programme lasts two years and provides fundamental nursing and midwifery skills. The GNM programme is a three-year diploma course that offers comprehensive training in general nursing and midwifery. The B.Sc. nursing programme is a four-year academic degree that imparts in-depth theoretical and practical knowledge in nursing (Indian Nursing Council 2024).

This study focuses on the GNM programme, as it constitutes the primary training pathway for the majority of nursing professionals in India, despite only a marginal numerical difference compared to Bachelor’s degree graduates. In 2021, 104,980 students were enrolled in the GNM programme, while 99,527 students were admitted to the B.Sc. Nursing programme (Ghosh 2022). Compared to the Bachelor’s programme, GNM training is characterised by a stronger practical orientation, making it more comparable to practice-based nursing training models in countries such as Germany (Indian Nursing Council 2024). The overarching goal of the GNM programme is to train nursing professionals who can work effectively and competently across all healthcare settings. Furthermore, the programme aims to support their personal and professional development, enabling them to contribute to disease prevention, health promotion, rehabilitation, and continuous professional education (Indian Nursing Council 2015). 

3.2       Germany

In Germany, multiple training pathways result in a qualification as a nurse. Following the implementation of the Nursing Professions Act (“Pflegeberufegesetz”) in 2020, the nursing training system underwent substantial modifications, primarily characterised by the standardisation of training programmes and the transition to a generalist approach (Federal Ministry of Health 2024). A fundamental distinction can be drawn between generalist nursing training and the academic path, which is pursued through nursing science or nursing education degree programmes (Schuppann et al. 2022).

Since 2020, generalist nursing training has superseded the former distinct training courses in healthcare, including geriatric nursing and paediatric nursing. The training programme to become a nurse is a three-year course that culminates in the state-recognised qualification of nursing specialist. This qualification confers the ability to work in all areas of care, including hospitals, nursing homes for the elderly and outpatient services. Within the generalist training programme, trainees have the option to specialise in geriatric nursing, healthcare or paediatric nursing during their third year (Federal Ministry of Health 2024). 

The academic path provides a range of career opportunities and is typically completed within a period of three to four years. Admissions criteria encompass the requirements for admission to universities of applied sciences or the general higher education entrance qualification, with the degree being designated as Bachelor of Science Nursing (Federal Ministry of Health 2024).

The present study focuses on the generalist training programme leading to the qualification of nursing specialist. This pathway represents the most frequently used entry route into professional nursing, with 61,458 trainees commencing their vocational training to become nursing specialists in Germany in 2021. In comparison, there are 1,091 students enrolled on comparable degree programmes (Meng et al. 2022).

4        Methods

The study examines the extent to which health promotion is integrated into nursing training in India and Germany. Therefore, the study employs a most-different research design, as India and Germany exhibit major differences in terms of culture, economy, and educational systems (Pilz 2012). Despite these differences, the clearly defined tertium comparationis, namely health promotion, allows for a systematic analysis of commonalities and differences without being confined to a national perspective (Pilz 2012). Consequently, the study employs an exploratory approach, as analysing curricula is necessary to determine the extent to which health promotion is embedded in nursing training programmes.

To analyse the curricula, this study employs the methodology of qualitative content analysis, which enables an empirical and intersubjectively verifiable examination that can be adapted to meet research objectives (Kuckartz 2014). The curricular documents examined include those widely used in nursing training in each country. For India, the study examines the “Syllabus and Regulations – Diploma in General Nursing & Midwifery” issued by the Indian Nursing Council (2015). To ensure that this document accurately represents the standard curriculum for nursing training in India, Indian nursing professionals were consulted regarding whether they had been trained according to this curriculum. For Germany, the study refers to the “Rahmenausbildungspläne der Fachkommission nach § 53 PflBG” (Curriculum of the expert commission according to § 53 PflBG) published by the Federal Institute for Vocational Education and Training (BIBB 2023). 

A central element of content analysis is developing categories either inductively or deductively. This study employs a hybrid approach that incorporates both deductive and inductive elements for the systematic analysis of curricula, allowing for the structured identification of predefined themes while maintaining flexibility to capture emergent content from the material. The combination of deductive and inductive approaches ensures an exploratory yet structured content analysis process (Kuckartz 2014). The deductively formed main categories include, on the one hand, “physical health promotion” and, on the other hand, “psychological/mental health promotion”. The category system with subcategories, which serves as the basis for the content analysis of the curricula, is shown in Table 1.

Table 1: Category system for curricula analysis 

Physical health promotion
Category	Definition	Example
Ergonomics, nutrition and exercise	Reduction of physical strain on trainees and the improvement of their physical health	“Describe the principles of nutrition and dietetics and their relationship to the humanbody in health and disease; Describe the balanced diet in promotion of health” (Indian Nursing Council 2015, 68)“Integrate measures for promoting one’s own health into everyday care activities and work processes and reflect on them using various examples (e.g. back-friendly working, reducing physical strain […])” (BIBB 2023, 31)
Workplace safety and prevention of work-related risk factors	Safety of trainees in their everyday work and the minimisation of potential health risks, such as infections	“Practice technique of wearing and removing Personal protective equipment (PPE); Practice Standard safety precautions (Universal precautions)” (Indian Nursing Council 2015, 58)“Take hygiene measures into account in nursing care; integrate basic health promotion and prevention measures into nursing care for self-care” (BIBB 2023, 24)
Psychological/mental health promotion
Category	Definition	Example
Stress management and resilience promotion	Immediate stress management with long-term approaches to strengthen personal resilience	“Stress and conflicts, natural sources and types of stress and conflict, frustration – sources and overcoming frustration” (Indian Nursing Council 2015, 42)“Methods for protection against physical and mental stress/stress management/reduction and resilience development, e.g. relaxation exercises, supervision, meditation, etc.” (BIBB 2023, 72)
Self-care and self-discovery	Trainees’ awareness of their own health and identity	“Ethics in Nursing-roles and responsibilities of a nurse” (Indian Nursing Council 2015, 46)“Reflection on one’s own specific health behaviour using health behaviour models and derivation of specific consequences for one’s own health-related behaviour and nursing actions” (BIBB 2023, 74)

To supplement the curricular analysis, semi-structured interviews with Indian nursing professionals were conducted using a guideline-based approach, with the category system developed in the curriculum analysis serving as the foundation for both the interview guide and the interview analysis (Schmidt 2004). Therefore, a mixed-methods design consisting of curriculum analysis and interviews was used for the Indian context. A total of three Indian nurses were interviewed via digital Zoom meetings. The contact with these professionals was facilitated through a collaboration between German researchers and Indian partners. Participation in the interview was entirely voluntary. The nursing staff were assured of the confidentiality and anonymity of their contributions. The ethical guidelines of the University of Cologne were followed. The average duration of the interviews was 25 minutes. As recommended in the literature, technical support was utilised for the transcription and coding process, employing the software MAXQDA (Kuckartz 2014).

5        Results

5.1       Results of the curriculum analysis

An analysis of the German curriculum identified seven text passages that reflect the subcategory of “ergonomics, nutrition, and exercise”. Notably, Germany places greater emphasis on “promoting and maintaining mobility” for nursing trainees (BIBB 2023, 36). Compared to the Indian curriculum, the German curriculum provides a more detailed description of the content to be taught, such as mobility- and development-promoting movement concepts and their effectiveness (BIBB 2023, 36). Specific strategies for personal health maintenance are discussed, including back-friendly work techniques for movement facilitation, patient transfer, and positioning in bed, as well as the adoption of health-promoting postures and training for strength, flexibility, endurance, and coordination (BIBB 2023, 211). These measures are intended to be integrated into daily nursing activities and work routines, encouraging reflective practice. Furthermore, the German curriculum mandates that prospective nurses “recognise their own limitations and competently utilise technical aids to support individuals with impaired mobility” (BIBB 2023, 40). In contrast, the Indian curriculum identifies three relevant content areas that leave room for interpretation, such as: “Implement effective nursing care by integrating scientific principles for maintaining health optimum” (Indian Nursing Council 2015, 53). The topic of a “balanced diet” is addressed more comprehensively in the Indian curriculum, with a dedicated subject on “principles of nutrition and dietetics and its relationship to the human body” (Indian Nursing Council 2015, 68). By comparison, the German curriculum appears to focus on nutrition primarily concerning patient health. Additionally, the Indian curriculum mentions “Physical Education/Yoga”, although this is not an integral part of the core curriculum but rather an extracurricular activity (Indian Nursing Council 2015, 18).

Regarding the subcategory of “workplace safety and prevention of work-related risk factors”, nine relevant passages were identified in the German curriculum, compared to two in the Indian curriculum. In both countries, hygiene and infection prevention are extensively covered. However, the German curriculum integrates “occupational safety measures” (BIBB 2023, 52), though these are not elaborated further. Similarly, the Indian curriculum addresses this area, including practical training on donning and doffing personal protective equipment, albeit with less prominence in the overall curriculum (Indian Nursing Council 2015, 58). Infection prevention in the Indian curriculum is described in greater detail, covering specific techniques such as “infection control; hand washing techniques; simple hand antisepsis and surgical antisepsis (scrub); prepare isolation unit in lab/ward” (Indian Nursing Council 2015, 57).

Seventeen text passages in the German curriculum address the subcategory of “stress management and resilience promotion”. A closer examination of these passages reveals that German trainees are given substantial attention in this area. For instance, the curriculum explicitly states that trainees should “consciously implement strategies for coping with and managing psychological stressors in complex nursing environments, inform themselves about institutional support services, and utilise them if necessary” (BIBB 2023, 229). This includes processing distressing experiences, particularly those involving emergencies with children and adolescents, as well as interactions with their caregivers and families (BIBB 2023, 89). Additionally, trainees are expected to encounter and reflect on experiences such as “irritation, uncertainty; stress and time pressure; frustration thresholds and tendencies toward violence; rejection, over-involvement; homophobia and (unconscious) heteronormativity; unfounded fears of self-infection (e.g., HIV-positive individuals)” (BIBB 2023, 153). Resilience-building is explicitly incorporated into the German curriculum and embedded in various fields, with mentions of relaxation exercises, supervision, and meditation (BIBB 2023, 53). Students are encouraged not only to experience and process their “own sense of powerlessness, helplessness, and stress/time pressure” but also to reflect on their reactions (BIBB 2023, 46 & 92): “They reflect on internal contradictions between the aspiration to help and the experience of disgust, shame, impatience, rejection, boundary violations, and helplessness” (BIBB 2023, 45). To support this, “initiatives for strengthening health literacy (adherence promotion, self-responsibility, coping, and empowerment)” are provided (BIBB 2023, 112). By contrast, the Indian curriculum contains three general references to stress management and resilience, for example: “Describe the concept of mental health and psychology” (Indian Nursing Council 2015, 41). Alongside the concept of mental health, the curriculum addresses coping with stress, conflict, and frustration. Emphasis is also placed on cultivating a positive attitude among future nurses (Indian Nursing Council 2015, 41f.)

The subcategory of “self-care and self-discovery” comprises 24 passages in the German curriculum and six in the Indian curriculum, representing the most extensive category in both countries and suggesting a relevant focus on this area. In the German curriculum, self-care is explicitly mentioned: “Students practice self-care and contribute to their own health maintenance, access support services, or seek assistance at their respective learning locations” (BIBB 2023, 38). Additionally, trainees “experience, interpret, and process beliefs regarding their own invulnerability; culturally influenced health convictions and self-efficacy expectations; feelings of competence regarding their health; well-being and perceived strength” (BIBB 2023, 52). Reflection on personal health behaviours and deriving consequences are central elements. Furthermore, trainees develop their professional identity and “a professional understanding of nursing” (BIBB 2023, 33), engaging with their professional values and ethical convictions. Additional topics include teamwork, interprofessional collaboration, role conflicts, role uncertainty, and lifelong learning as an essential component of personal and professional development (BIBB 2023, 49). Similarly, the Indian curriculum emphasises the “need for continuing education for professional development” (Indian Nursing Council 2015, 53). However, a notable distinction is that while the German curriculum places strong importance on the individual student, the Indian curriculum highlights the societal role of nurses: “To help nurses in their personal and professional development, so that they are able to make maximum contribution to society as useful and productive individuals, citizens as well as efficient nurses” (Indian Nursing Council 2015, 8). Nursing trainees are encouraged to “break bad habits” and cultivate “good habit”, which are deemed essential for the nursing profession (Indian Nursing Council 2015, 42). 

The comparative content analysis reveals differences between the German and Indian nursing curricula regarding the emphasis and structure of health promotion for trainees. The findings indicate that Germany allocates more space within the curriculum to health promotion, both quantitatively and qualitatively. While Germany explicitly addresses specific measures and strategies for the physical and psychological health of trainees, the Indian curriculum appears to focus more on the societal function of nurses. This suggests a fundamental cultural distinction: Germany places greater value on individual well-being, whereas India emphasises societal roles and responsibilities.

Overall, Germany includes more content in all categories of health promotion, particularly in the domain of psychological health, where specific strategies for stress management and resilience-building are elaborated. However, the Indian curriculum also exhibits unique features, particularly in the field of nutrition, which holds a higher priority compared to the German curriculum.

5.2       Results of the interviews on India

The interviews conducted highlighted that, in some cases, a discrepancy exists between the content prescribed in the curriculum and the content taught. The respondents’ statements differ regarding the specific areas in which these discrepancies occur. These findings are in line with other study results, also indicating that teachers in India, for various reasons, do not always teach what the curricula prescribe (e.g., Zenner et al. 2017). 

In the domain of physical health promotion and the subcategory “ergonomics, nutrition, and exercise”, initial differences become apparent. While two of the three interviewed nurses emphasised that topics such as lifting techniques for back-friendly work were covered, one respondent expressed a critical perspective: “We know how to tend patient. How to carry patient. But not enough. That is not enough. That’s why we have back pain and shoulder pain” (Interview 2). This statement underscores the need for a more comprehensive integration of the topic. This is corroborated by another interview. Although the nurse in this case did not take as explicit a stance as in the aforementioned quote, it was mentioned that posture and related aspects were only briefly addressed in the first year of training (Interview 2). Regarding nutrition, the interviews also reveal differing perspectives. Two of the three respondents confirmed the written curriculum and emphasised that the importance of a healthy and balanced diet was repeatedly conveyed, both to patients and to trainees (Interview 2). However, the remaining respondent stated that this area was not covered in depth: “I am just saying that about the nutritional part only, that is the lacking part” (Interview 1). All interviewed nurses agreed on the absence of physical activities. Only one interview confirmed that such activities were offered sparsely (Interview 1). All respondents expressed a desire for a more extensive integration of physical activity into training.

The content delivered under the subcategory “workplace safety and prevention of work-related risk factors” largely corresponds to the curriculum, according to the interviews. For instance, the use of protective clothing is addressed, and the topic of infection prevention is comprehensively covered (Interviews 2 and 3). One interview also indicated that healthcare facilities provide the necessary protective clothing in accordance with the curriculum (Interview 1).

Within the main category of psychological health promotion, the subcategory “stress management and resilience promotion” emerged as particularly critical in the interviews. The following statement is noteworthy in response to the question of whether stress management was addressed: “Our only technique is facing more stress” (Interview 1). This quote reflects a theme that is evident across all interviews: “We don’t have stress management classes or anything” (Interview 2). “They should have given proper education […] stress wasn’t a basic thing. It was more about, like, you care for the patient” (Interview 3). Consequently, the respondents expressed a desire to integrate stress management into both training and practical work (Interview 2).

In the subcategory “self-care and self-discovery”, the interviewed nurses stated that the implemented content largely aligns with the curriculum. However, in terms of professional identity, it appears that the curriculum places greater emphasis on societal well-being than on the well-being of trainees themselves: “We should have some intention. Good intention in this job. More than good salary and good life” (Interview 1). A certain degree of ‘self-sacrifice’ resonates in this statement. As previously mentioned, a similar interpretation can also be derived from the Indian curriculum.

In summary, the findings of these few interviews indicate that the objectives and content outlined in the curriculum are not always fully realised in India. In particular, the area of stress management exhibits discrepancies, as it is absent in practice, according to the interviewed nurses.

6        Discussion

The comparative analysis of health promotion in curricula in India and Germany reveals notable discrepancies in the emphasis and organisation of health promotion for trainees. Germany’s approach is characterised by a more systematic, detailed and comprehensive handling of health promotion aspects. The German curriculum demonstrates a congruence with international studies on mental health promotion for trainees (Proper & van Oostrom 2019; Schaller et al. 2022). In terms of the six dimensions of health promotion (Walker et al. 1987), it can be concluded that in Germany, the aspects of health responsibility, interpersonal relationships and stress management are prioritised, while in India, nutrition is a predominant aspect alongside health responsibility.

A potential explanation for these discrepancies can be found in the cultural influences on the curricula in both countries (Ashbee 2021). Existing literature has explored the cultural divergences between Germany and India (e.g., Juhász 2014). A seminal concept in analysing cultural differences is Hofstede’s (2001) model of cultural dimensions, which identifies various dimensions, including individualism and collectivism, that offer insights into the value systems in disparate societies. According to Hofstede’s model, Germany is defined as an individualistic society, while India is classified as a collectivist society. This classification suggests that Germany’s fundamental cultural attitude may lead to a greater emphasis on individual career and health values, while India’s health promotion is more oriented towards patients and society as a whole. It is also noteworthy that, in contrast to Germany, the concept of occupational health and safety in India is generally less pronounced (Dasgupta et al. 2017; Chellappa et al. 2021). 

This phenomenon is further compounded by the ongoing high demand for nurses in Germany (The Federal Government 2024), while certain regions of India experience a surplus of skilled workers in the healthcare sector (Khadria & Tokas 2022). This imbalance may also influence the perception of the profession (Nair 2012), potentially diminishing its attractiveness and relevance to health promotion initiatives. 

7        Conclusion

The present study analysed health promotion for nursing trainees in Germany and India to ascertain its integration within respective curricula. Specifically, the research question investigated how health promotion is integrated into the curricula of German and Indian nursing training and to what extent these differ. The findings are both innovative, as no detailed studies have been conducted in this area before, and practically relevant, as they can help to guide recognition processes and, if necessary, additional qualification measures in the context of nurse migration to Germany.

In summary, the German curriculum addresses the topic of health promotion for nursing trainees more frequently and in greater depth. In contrast, the Indian curriculum focuses more on general concepts and emphasises the role of nurses as productive and valuable members of society. The area of nutrition is covered in more detail in the Indian curriculum than in the German one. Consequently, both countries can learn from each other and use the respective strengths of their curricula as inspiration for future improvements in health promotion for nursing trainees. For instance, the German curriculum could incorporate aspects of nutrition, while the Indian curriculum could include components on mental health for nursing students. The challenges experienced by nursing staff in India are not unique to this country; such difficulties are prevalent in other regions of the world. In the context of international professional mobility, it is imperative that Indian nursing professionals possess the requisite competencies to effectively manage and mitigate mental distress (Jadhav & Roy 2024).

One limitation of this study that must be acknowledged is the differing availability of sources. In Germany, the various nursing training programmes have been consolidated into a generalist training model under the Nursing Professions Act, making the choice of curriculum to be examined clear. However, for India, a specific curriculum had to be selected for analysis. Based on the outlined reasoning, this study opted for the curriculum of the GNM diploma programme. In India, the number of enrolments in the B.Sc. Nursing and the GNM programme are much closer than the respective figures for nursing vocational training and nursing studies in Germany. Therefore, it may be useful for future research also to analyse the content of the B.Sc. Nursing curriculum in India concerning health promotion. For future studies, expanding the sample size in India and extending the investigation to other learning areas within the profession would be beneficial. Additionally, conducting interviews among German nursing professionals could provide a more detailed comparison between the prescribed curriculum and its practical implementation. A critical analysis is required to ascertain the extent to which learners are genuinely empowered during their training to promote their health in the workplace proactively.

References

Alpar, Ş., Şenturan, L., Karabacak, Ü., & Sabuncu, N. (2008). Change in the health promoting lifestyle behaviour of Turkish University nursing students from beginning to end of nurse training. In: Nurse Education in Practice, 8, 6, 382–388. Online: https://doi.org/10.1016/j.nepr.2008.03.010 (retrieved 12.07.2025).

Alzahrani, S., Malik, A., Bashawri, J., Shaheen, S., Shaheen, M., Alsaib, A., Mubarak, M., Adam, Y., Mubarak, M., Adam, Y., & Abdulwassi, H. (2019). Health-promoting lifestyle profile and associated factors among medical students in a Saudi university. In: SAGE Open Medicine, 7, 1–7. Online: https://doi.org/10.1177/2050312119838426 (retrieved 12.07.2025).

Ashbee, R. (2021). Curriculum: Theory, Culture and the Subject Specialisms. London: Routledge.

Babapour, A.-R., Gahassab-Mozaffari, N., & Fathnezhad-Kazemi, A. (2022). Nurses’ job stress and its impact on quality of life and caring behaviors: a cross-sectional study. In: BMC Nursing, 21, 75, 1–10. Online: https://doi.org/10.1186/s12912-022-00852-y (retrieved 12.07.2025).

BIBB. (2023). Rahmenausbildungspläne der Fachkommission nach § 53 PflBG. Online: https://www.bibb.de/dienst/publikationen/de/16560 (retrieved 17.03.2025).

Biebeler, H., Körbel, M., & Pierenkemper, S. (2016). Rekrutierung internationaler Krankenpflegekräfte – berufliche Anerkennung oder Ausbildung in Deutschland. In: BWP: Berufsbildung in Wissenschaft und Praxis, 3, 48–51.

Chellappa, V., Srivastava, V., & Salve, U. (2021). A systematic review of construction workers’ health and safety research in India. In: Journal of Engineering, Design and Technology, 19, 6, 1488–1504. Online: https://doi.org/10.1108/JEDT-08-2020-0345 (retrieved 12.07.2025).

Chinengundu, T. (2025). Analysis of the South African Construction Technology Curriculum and Assessment Policy Statement Vocational Didactics. In: TVET@Asia, 24, 1–16. Online: https://tvet-online.asia/startseite/vocational-training-in-the-indian-construction-sector-analyzing-curriculum-demographic-profiles-and-regional-disparities/(retrieved 12.07.2025).

Dasgupta, J., Velankar, J., Borah, P., & Nath, G. (2017). The safety of women health workers at the frontlines. In: Indian Journal of Medical Ethics, 2, 3, 209–213. Online: https://doi.org/10.20529/IJME.2017.043 (retrieved 12.07.2025).

Datta, A. & Basu, A. (2023). Do the Kerala nurses in Germany break the myth of migration as a male-space? In: Migration and Diversity, 2, 3, 299–309. Online: https://doi.org/10.33182/md.v2i3.3066 (retrieved 12.07.2025).

Dukpa, U. & Pattanaik, F. (2025). Vocational Training in the Indian Construction Sector: Analyzing Curriculum, Demographic Profiles, and Regional Disparities. In: TVET@Asia, 24, 1–16. Online: https://tvet-online.asia/startseite/analysis-of-the-south-african-construction-technology-curriculum-and-assessment-policy-statement-vocational-didactics/ (retrieved 12.07.2025).

Federal Ministry of Health. (2024). Pflegeberufegesetz. Online: https://www.bundesgesundheitsministerium.de/pflegeberufegesetz.html (retrieved 27.01.2025).

Fiabane, E., Giorgi, I., Sguazzin, C., & Argentero, P. (2013). Work engagement and occupational stress in nurses and other healthcare workers: the role of organisational and personal factors. In: Journal of Clinical Nursing, 22, 17-18, 2614–2624. Online: https://doi.org/10.1111/jocn.12084 (retrieved 12.07.2025). 

Ghosh, A. (2022). Nursing courses see Covid boom, 99% of BSc seats taken in 2020-21, 91% in diploma. Online: https://theprint.in/health/nursing-courses-see-covid-boom-99-of-bsc-seats-taken-in-2020-21-91-in-diploma/826147/(retrieved 27.01.2025).

Goel, U. (2013). Heteronormativity and intersectionality as perspective of analysis of gender and migration: Nurses from India in West Germany. In Poma, S. & Pühl, K. (eds.): Perspectives on Asian Migration: Transformations of Gender and Labour Relations. Berlin: Rosa-Luxemburg Stiftung, 77–82.

Großmann, D., Wochnik, M., Reiber, K., Reuschenbach, B., & Olden, D. (2023). Intendierte und realisierte Umsetzung der generalistischen Pflegeausbildung am Beispiel der Praxisanleitung. Eine Triangulation von Zwischenergebnissen der Begleitforschung. In Kögler, D., Kremer, H.-H. & Herkner, V. (eds.): Jahrbuch der berufs- und wirtschaftspädagogischen Forschung 2023. Opladen, Berlin, Toronto: Verlag Barbara Budrich, 120–140. Online: https://doi.org/10.3224/84742719 (retrieved 12.07.2025).

Heinen, M., van Achterberg, T., Schwendimann, R., Zander, B., Matthews, A., Kózka, M., Ensio, A., Sjetne, I., Moreno, T., Ball, J., & Schoonhoven, L. (2013). Nurses’ intention to leave their profession: A cross sectional observational study in 10 European countries. In: International Journal of Nursing Studies, 50, 2, 174–184. Online: https://doi.org/10.1016/j.ijnurstu.2012.09.019 (retrieved 12.07.2025).

Hofstede, G. (2001). Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. Thousand Oaks: SAGE Publications.

Indian Nursing Council. (2015). Syllabus and Regulations: Diploma in General Nursing & Midwifery. Online: https://www.dooncollege.edu.in/Medical/gnm-1.pdf (retrieved 17.03.2025).

Indian Nursing Council. (2024). Nursing Programs. Online: https://indiannursingcouncil.org/nursingprograms(retrieved 17.03.2025).

Jadhav, ST. & Roy, SM. (2024). The Sociodemographic Characteristics of Indian Nursing Students and Their Intentions to Migrate Overseas for Work. In: Policy, Politics, & Nursing Practice, 25, 1, 58–62. Online: https://doi.org/10.1177/15271544231201347 (retrieved 12.07.2025).

Jakobs, A. & Vogler, C. (2020). Generalistische Ausbildung: Ein Jahr “neue Pflege.” In: Pflegezeitschrift, 73, 12, 38–41. Online: https://doi.org/10.1007/s41906-020-0949-7 (retrieved 12.07.2025).

Juhász, I. (2014). The Workforce in Indian Organizations: An Analysis Based Upon the Dimensions of Hofstede’s Model. In: Karlovitz, J. (ed.): Economics Questions, Issues and Problems. Komárno: International Research Institute, 38–45.

Khadria, B. & Tokas, S. (2022). Aspirations of Health Professionals in India for Migration Abroad: A Pre-COVID and COVID-Time Comparison of Nurses. In: Rajan, S. (ed.): India Migration Report 2022. London: Routledge India, 209–229. Online: https://doi.org/10.4324/9781003315124-11 (retrieved 12.07.2025).

Kuckartz, U. (2014). Qualitative text analysis: a guide to methods, practice and using software. London: SAGE Publications.

Magnavita, N. (2014). Workplace violence and occupational stress in healthcare workers: a chicken-and-egg situation-results of a 6-year follow-up study. In: Journal of Nursing Scholarship, 46, 5, 366–376. Online: https://doi.org/10.1111/jnu.12088 (retrieved 12.07.2025).

Mak, Y., Kao, A., Tam, L., Tse, V., Tse, D., & Leung, D. (2018). Health-promoting lifestyle and quality of life among Chinese nursing students. In: Primary Health Care Research & Development, 19, 6, 629–636. Online: https://doi.org/10.1017/S1463423618000208 (retrieved 12.07.2025).

McElligott, D., Siemers, S., Thomas, L., & Kohn, N. (2009). Health promotion in nurses: is there a healthy nurse in the house? In: Applied nursing research, 22, 3, 211–215. Online: https://doi.org/10.1016/j.apnr.2007.07.005 (retrieved 12.07.2025). 

Melariri, H., Osoba, T., Williams, M., & Melariri, P. (2022). An assessment of nurses’ participation in Health Promotion: a knowledge, perception, and practice perspective. In: Journal of Preventive Medicine and Hygiene, 63, 1, 27–34. Online: https://doi.org/10.15167/2421-4248/jpmh2022.63.1.2209 (retrieved 12.07.2025).

Meng, D., Peters, D., & Dorin, D. (2022). Erste Sondererhebung des BIBB-Pflegepanels : Ein aktueller Überblick zu berufsqualifizierenden Pflegestudiengängen. Bonn: BIBB.

Mojtahedzadeh, N., Bernburg, M., Rohwer, E., Nienhaus, A., Groneberg, D., Harth, V., & Mache, S. (2022). Health Promotion for Outpatient Careworkers in Germany. In: Healthcare, 10, 6, 1–23. Online: https://doi.org/10.3390/healthcare10061148 (retrieved: 12.07.2025).

Mooney, B., Timmins, F., Byrne, G., & Corroon, A. (2011). Nursing students’ attitudes to health promotion to: Implications for teaching practice. In: Nurse Education Today, 31, 8, 841–848. Online: https://doi.org/10.1016/j.nedt.2010.12.004 (retrieved 12.07.2025).

Nair, S. (2012). Moving with the Times: Gender, Status and Migration of Nurses in India. New Delhi: Routledge India.

Oda, H., Tsujita, Y., & Irudaya Rajan, S. (2018). An analysis of factors influencing the international migration of Indian nurses. In: Journal of International Migration and Integration, 19, 3, 607–624. Online: https://doi.org/10.1007/s12134-018-0548-2 (retrieved 12.07.2025).

Olden, D., Großmann, D., Dorin, L., Meng, M., Peters, M., & Reuschenbach, B. (2023). Die generalistische Pflegeausbildung in Deutschland aus Sicht Auszubildender. In: Pflege, 36, 5, 249–257. Online: https://doi.org/10.1024/1012-5302/a000930 (retrieved 12.07.2025).

Otto, A.-K., Bischoff, L., & Wollesen, B. (2019). Work-Related Burdens and Requirements for Health Promotion Programs for Nursing Staff in Different Care Settings: A Cross-Sectional Study. In: International Journal of Environmental Research and Public Health, 16, 19, 1–13. Online: https://doi.org/10.3390/ijerph16193586 (retrieved 12.07.2025).

Pilz, M. (2012). International comparative research into Vocational Training: Methods and Approaches. In Pilz, M. (ed.): The Future of Vocational Education and Training in a Changing World. Wiesbaden: Springer, 561–588.

Pilz, M. & Regel, J. (2021). Vocational Education and Training in India: Prospects and Challenges from an Outside Perspective. In: Margin: The Journal of Applied Economic Research, 15, 1, 101–121. Online: https://doi.org/10.1177/0973801020976606 (retrieved 12.07.2025).

Proper, K. & van Oostrom, S. (2019). The effectiveness of workplace health promotion interventions on physical and mental health outcomes – a systematic review of reviews. In: Scandinavian Journal of Work, Environment & Health, 45, 6, 546–559. Online: https://doi.org/10.5271/sjweh.3833 (retrieved 12.07.2025).

Reiff, E., Gade, C., & Böhlich, S. (2020). Handling the shortage of nurses in Germany: Opportunities and challenges of recruiting nursing staff from abroad. IUBH Discussion Papers – Human Resources, 3/2020. Online: https://www.econstor.eu/bitstream/10419/222921/1/1726039005.pdf (retrieved 12.07.2025).

Schaller, A., Gernert, M., Klas, T., & Lange, M. (2022). Workplace health promotion interventions for nurses in Germany: a systematic review based on the RE-AIM framework. In: BMC Nursing 21, 65, 1–17. Online: https://doi.org/10.1186/s12912-022-00842-0 (retrieved 12.07.2025).

Schmidt, C. (2004). The Analysis of Semi-structured Interviews. In Flick, U., von Kardorff, E. & Steinke, I. (eds.): A companion to qualitative research. London, Thousand Oaks, New Delhi: SAGE Publications, 253–258.

Schneider, S. & Pilz, M. (2024). India’s labour market challenges: Employability of young workforce from the perspective of supply and demand. In: PROSPECTS, 54, 687–708. Online: https://doi.org/10.1007/s11125-024-09691-y(retrieved 12.07.2025).

Schneider, S., Wessels, A., & Pilz, M. (2023). Theory and practice of teaching and learning in the classroom – lessons from Indian Industrial Training Institutes. In: Vocations and Learning, 16, 99–120. Online: https://doi.org/10.1007/s12186-022-09305-8 (retrieved 12.07.2025).

Schuppann, S., Stutzer, F., & Militzer-Horstmann, C. (2022). Professionelle Pflege in Deutschland. In: Forschungsberichte des Wissenschaftlichen Instituts für Gesundheitsökonomie und Gesundheitssystemforschung 12, 73–135. Online: https://doi.org/10.60019/UESN3136 (retrieved 12.07.2025).

Stanulewicz, N., Knox, E., Narayanasamy, M., Shivji, N., Khunti, K., & Blake, H. (2020). Effectiveness of Lifestyle Health Promotion Interventions for Nurses: A Systematic Review. In: International Journal of Environmental Research and Public Health, 17, 1, 1–17. Online: https://doi.org/10.3390/ijerph17010017 (retrieved 12.07.2025).

The Federal Government. (2024). Fachkräftestrategie Indien. Online: https://www.bundesregierung.de/breg-de/aktuelles/fachkraeftestrategie-indien-2314888 (retrieved 17.03.2025). 

von Ungern-Sternberg, H. (2025). Indische Auszubildende für das Handwerk: so kann es klappen. In Schwaab, M. & Durian, A. (eds.): Zeitarbeit und Personaldienstleistungen: Chancen, Erfahrungen, Herausforderungen. Wiesbaden: Springer, 375–381.

Walker, S., Sechrist, K., & Pender, N. (1987). The Health-Promoting Lifestyle Profile: development and psychometric characteristics. In: Nursing Research, 36, 2, 76–81.

Walton-Roberts, M. & Irudaya Rajan, S. (2023). Nurse emigration from Kerala: Revisiting the ‘brain circulation’ or ‘trap’ question. In John, M. & Wichterich, C. (eds.): Who cares? Care extraction and the struggles of Indian health workers. New Delhi: Zubaan, 185–212.

Wesselborg, B. & Bauknecht, J. (2025). Psychische Erschöpfung in den Pflegeberufen: eine quantitative Querschnittstudie zu Belastungs- und Resilienzfaktoren. In: Prävention und Gesundheitsförderung, 20, 1, 141–147. Online: https://doi.org/10.1007/s11553-023-01085-w (retrieved 12.07.2025).

Wessels, A. & Pilz, M. (2018). International Handbook of Vocational Education and Training: India. Bonn: Verlag Barbara Budrich.

Whitehead, D. (2007). Reviewing health promotion in nursing education. In: Nurse Education Today, 27, 3, 225–237. Online: https://doi.org/10.1016/j.nedt.2006.05.003 (retrieved 12.07.2025).

WHO. (1986). Ottawa Charter for Health Promotion. Online: https://www.who.int/teams/health-promotion/enhanced-wellbeing/first-global-conference  (retrieved 17.03.2025). 

Wichterich, C. (2024). From angels to fighters: Indian nurses in Germany from the 1960s till today. In: Migration and Development, 13, 2, 1–19. Online: https://doi.org/10.1177/21632324241307074 (retrieved 12.07.2025).

Wittenborg, A. & Wollnik, C. (2014). Fachkräftemigration aus Asien nachhaltig und fair gestalten: Im Dialog mit Indien und Vietnam. In Kreienbrink, A. (ed.): Fachkräftemigration aus Asien nach Deutschland und Europa. Nuremburg: Federal Office for Migration and Refugees, 205–235.

Wochnik, M., Tsarouha, E., Krause-Zenß, A., Greißl, K., & Reiber, K. (2022). Lernortkooperation als besondere Anforderung in den neuen Pflegeausbildungen. In Kögler, K., Weyland, U., & Kremer, H.-H. (eds.): Jahrbuch der berufs- und wirtschaftspädagogischen Forschung 2022. Opladen, Berlin, Toronto: Verlag Barbara Budrich, 261–273.

Zenner, L., Kothandaraman, K., & Pilz, M. (2017). Entrepreneurship education at Indian Industrial Training Institutes – a case study of the prescribed, adopted and enacted curriculum in and around Bangalore. In: International Journal for Research in Vocational Education and Training, 4, 1, 69–94. Online: https://doi.org/10.13152/IJRVET.4.1.4 (retrieved 12.07.2025).