Hybrid Regional Training Programme on AI Enhanced Curriculum & Lesson Planning in TVET (On-site)

Rationale

Generative Artificial Intelligence (GenAI) is rapidly reshaping education and the world of work. With AI projected to contribute USD 15.7 trillion to the global economy by 2030 (PwC, 2021) and the World Economic Forum (2023) forecasting the net creation of 97 million new roles requiring AI literacy, TVET systems across ASEAN must produce graduates who are technically skilled and confident in AI-augmented workplaces. Responding to this mandate begins with the TVET educator. UNESCO’s AI Competency Framework for Educators (2021) calls for technical competence, pedagogical integration, professional ethics, and institutional leadership. Yet most ASEAN TVET teachers have encountered AI tools only informally, without the foundational understanding needed to deploy them responsibly — leaving them ill-equipped to lead their students through the AI transition.

This course closes that gap above systematically, building from AI and GenAI fundamentals through prompt engineering — the core skill for communicating effectively with AI systems (Mollick & Mollick, 2023) — to full application of the ADDIE instructional design model (Branch, 2009). Participants apply purpose-fit GenAI tools at every ADDIE phase and for content and presentation development, culminating in a complete AI-enhanced TVET lesson plan. Ethics and responsible AI — hallucination, bias, data privacy, and academic integrity — are embedded throughout, not added as an afterthought. The programme aligns with UNESCO’s AI Competency Framework for Educators (2021), ASEAN’s Digital Masterplan 2025, and SEAMEO VOCTECH’s mandate to strengthen regional TVET capacity.

Objectives

• Explain key Artificial Intelligence (AI) concepts and analyze their relevance to the transformation of Technical and Vocational Education and Training (TVET) systems.   

• Apply advanced prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought) to generate high-quality educational content. 

• Evaluate and operate selected AI tools in instructional design, assessment creation, and student engagement strategies.   

• Formulate a comprehensive action plan for integrating AI solutions into specific teaching practices and institutional processes.

Contents

Module 1: The Fundamental of AI & Generative AI

This module introduces the core concepts of Generative AI, distinguishing it from traditional machine learning. Participants explore foundation models (LLMs), tokenization, and embeddings. The session establishes a baseline understanding of how models predict and generate data, setting the stage for practical application.

Module 2: Prompt Engineering

This module defines prompts as the “programming language” of LLMs, emphasizing clarity, specificity, and persona assignment. Participants learn to design structural prompts to improve AI outputs, ensuring they are sound and accurate.

Module 3: Curriculum Development Overview: Cycle and Available Models, Frameworks, Techniques

This module provides an overview of the different components in the curriculum development cycle and the widely use model and technique such as ADDIE and DACUM.

Module 4: Lesson Plan Development Overview: Cycle and Available Models, Frameworks, Techniques

This module shows differences between the curriculum development and lesson plan development and the widely use models in lesson plan development. Understanding by Design (UbD) / Backward Design model will be explored in more detail.

Module 5: AI in Curriculum and Lesson Plan Development: Opportunities, Risks, Ethics, and Mitigations

This module positions AI as a powerful tool for curriculum and lesson plan development. It shows the opportunities of AI not only speed up the process, but it could also enhance the output and yet taking potential risks into account. Awareness of ethical and responsible AI as well as it risks mitigation.

Module 6: Generative AI Tools For Curriculum Developer 1: Research and Analytics

In developing a curriculum and lesson plan, one should do a (deep) research and analysis. This module introduces the available generative AI tools in the market for doing so. These tools will be used during the hands-on.

Module 7: Generative AI Tools For Curriculum Developer 2: Delivery Enchancement

This module explores tools for creating high-impact presentations and multimedia content, Both through a (chain-of-) prompt(s) or a ready-to-use generative AI for a special purpose.

Duration

The training programme will run for 9 days, divided into two parts: the first part consists of 3 days which will be held virtually from 11 to 13 August 2026, and the second part of 6 days will be held face-to-face at SEAMEO VOCTECH Regional Centre from 17 to 22 August 2026.

Delivery Method 

The medium of instruction for this programme is English. For the online session, the course content will be delivered synchronously through online presentations, lectures, and demonstrations. Participants are expected to engage actively by sharing presentation screens and interacting in online activities. Selected participants are also required to present their country reports online. The face-to-face sessions will focus on lectures, demonstrations, hands-on practice, discussions, assignments, and group projects.

Expected Output 

By the end of this programme, participants are expected to produce the following tangible outputs: 

1. An AI-enhanced TVET Curriculum Module — a structured curriculum component for a selected TVET subject area, developed using the ADDIE model with generative AI tool assistance and prompts at each phase, incorporating Bloom’s Taxonomy-aligned learning objectives, content outline, and assessment plan. 
2. An AI-enhanced TVET Lesson Plan — a complete lesson plan derived from the curriculum module above, developed using a recognised lesson plan framework (e.g., UbD/Backward Design), incorporating AI-generated instructional materials, formative assessments, and multimedia learning resources produced using generative AI tools and prompts. 
3. An Institutional AI Integration Action Plan — a structured plan specifying concrete steps, selected AI tools, capacity building strategies, risk mitigation measures, and a phased implementation timeline for integrating AI-enhanced curriculum and lesson planning practices at the participant’s home institution. 

Target Participants 

This training programme is designed for lecturers, teachers, trainers, and leaders in Technical and Vocational Education and Training (TVET). It is also ideal for educators, instructional designers, and technology enthusiasts who are interested in creating and integrating AI into their teaching practices. No advanced technical background is required —participants with basic IT skills (such as the ability to operate MS Windows and use Microsoft Office tools) are encouraged to join and will be supported throughout the training with hands-on guidance and practical applications. 

Course Requirements 

Participants are required to submit all assignments given and present the outputs of their group projects during this training programme. They must also submit a country report paper that describes the practices currently being undertaken by their respective institutions or countries in integrating AI into teaching and learning including in curriculum and lesson plan developments. Participants should bring along a laptop computer with at least Windows 10 installed. 

Participants must also bring one of their latest lesson plans and current curriculum from their institution

References 

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s educational objectives. Longman.  

ASEAN Secretariat. (2021). ASEAN digital masterplan 2025. https://asean.org 

Bell, S. C. (2023). Using ChatGPT to create Bloom’s taxonomy-based learning objectives. The Journal of Physician Assistant Education, 34(4), 329–332. https://doi.org/10.1097/JPA.0000000000000551 

Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6  

Davis, A. L. (2013). Using instructional design principles to develop effective information literacy instruction: The ADDIE model. College & Research Libraries News, 74(4), 205–207. https://doi.org/10.5860/crln.74.4.8934  

Dickey, E. (2024). GAIDE: A framework for using generative AI to assist in curriculum design and education. In Proceedings of the IEEE Frontiers in Education Conference. IEEE. https://ieeexplore.ieee.org/document/10893132 

Frontiers in Education. (2025). Ethical and regulatory challenges of generative AI in education. Frontiers in Education, 10, Article 1565938. https://doi.org/10.3389/feduc.2025.1565938  

Habiballa, H., Kotyrba, M., Volna, E., Bradac, V., & Dusek, M. (2025). Artificial intelligence (ChatGPT) and Bloom’s taxonomy in theoretical computer science education. Applied Sciences, 15(2), Article 581. https://doi.org/10.3390/app15020581  

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868  

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.  

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(4), 212–218. https://doi.org/10.1207/s15430421tip4104_2  

McNulty, N. (2025). How to integrate Bloom’s taxonomy with generative AI [PDF]. https://www.niallmcnulty.com/wp-content/uploads/2025/02/AI_Blooms_Taxonomy.pdf 

MIT Sloan EdTech. (2025). When AI gets it wrong: Addressing AI hallucinations and bias. https://mitsloanedtech.mit.edu/ai/basics/addressing-ai-hallucinations-and-bias/ 

 Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts [Working paper]. The Wharton School, University of Pennsylvania. https://doi.org/10.2139/ssrn.4391243 

Norton, R. E. (1997). DACUM handbook (2nd ed.). Center on Education and Training for Employment, Ohio State University. (ERIC Document Reproduction Service No. ED401483) 

Ong, J., Montalan, M. A., Samaniego, A., & Abante, M. (2025). Leveraging generative AI for course learning outcome categorization. Computers and Education: Artificial Intelligence, 8, Article 100044X. https://doi.org/10.1016/j.caeai.2025.100044X 

Park, H., & Kim, M. (2024). Generative AI-powered instructional design: A new paradigm for creating effective learning environments. British Journal of Educational Technology, 55(2), 456–472. https://doi.org/10.1111/bjet.13390  

PwC. (2021). Global artificial intelligence study: Exploiting the AI revolution. PricewaterhouseCoopers. https://www.pwc.com/gai 

Spatioti, A. G., Kazanidis, I., & Pange, J. (2022). A comparative study of the ADDIE instructional design model in distance education. Information, 13(9), Article 402. https://doi.org/10.3390/info13090402  

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 

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UNESCO. (2021a). AI competency framework for educators. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000377071 

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Course Coordinator 

For further inquiries, please contact the Course Coordinator: 

Dr. Ir. Muhammad Ikhwan Jambak, MEng
TVET Specialist cum Knowledge Management Manager
SEAMEO VOCTECH Regional Centre 
Jalan Pasar Baharu, Gadong BE1318 
Negara Brunei Darussalam  

Email: ikhwan.jambak@voctech.edu.bn
Tel: (673) 2452267 |Fa x: (673) 2455072 | www.voctech.org