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).
