Generative AI in Education and Course Development
Signature Program
A two-day practical programme for educators and course developers using GenAI, NotebookLM, and related tools to build better learning materials and course assets.
Programme Positioning
A two-day practical programme for educators and course developers using GenAI, NotebookLM, and related tools to build better learning materials and course assets.
The Problem We're Solving
Many educators now have access to AI tools, but they still need a practical way to turn those tools into useful course development support.
The gap is not curiosity. It is workflow.
This programme helps participants use AI in a structured way to support teaching, learning design, assessment creation, and micro-course development.
Who This Is For
- Educators and course developers
- Trainers and instructional support roles
- Teams building content, CLOs, rubrics, and assessments
- Users who need practical AI support for education design work
Programme Overview
This is a two-day outline that moves from GenAI foundations into course development tools, then into ethics, governance, and a final build challenge.
The design stays practical and hands-on so participants leave with actual outputs rather than only conceptual understanding.
The Learning Journey
The journey helps educators move from AI curiosity into a practical course-development workflow. Learners explore GenAI, apply it to instructional design tasks, organise references, consider responsible use, and build a micro-course output that can be refined after the session.
Day 1
| Time | Topic |
|---|---|
| 0900am - 1030am | Introduction to GenAI in Education Overview Participants understand what GenAI can do in educational and instructional work. The block clarifies where AI should support judgement rather than replace it. Learners compare useful AI support with risky or shallow education outputs. Learning Outcome - Explain practical GenAI use in education. - Identify where human review remains essential. - Recognise weak AI output before using it with learners. |
| 1030am - 1045am | Morning Break |
| 1045am - 1245pm | Course Development Support Overview Participants use AI to generate ideas, draft content, and support course design tasks. The block covers topics, learning outcomes, activities, and assessment prompts. Participants refine outputs so the course design fits real learner needs and institutional standards. Learning Outcome - Use AI to draft course design components. - Refine AI output for learner needs and standards. - Align topics, outcomes, activities, and assessment more clearly. |
| 1245pm - 1400pm | Lunch & Prayer |
| 1400pm - 1530pm | NotebookLM and Reference Work Overview Participants learn how document-based AI can organise notes, articles, reports, and course references. The focus is on building a more usable course binder. They practise retrieving, summarising, and connecting source material for course development. Learning Outcome - Organise course source material more effectively. - Use document-based AI to retrieve and summarise references. - Connect source material to teaching and learning decisions. |
| 1530pm - 1545pm | Evening Break |
| 1545pm - 1700pm | Slide and Visual Support Overview Participants explore tools that support slide generation and visual mapping. The block helps them turn learning content into clearer teaching materials. The debrief focuses on whether the visual output supports learning or only looks attractive. Learning Outcome - Convert course content into slide or visual structures. - Evaluate whether generated visuals support the learning message. - Improve teaching materials with clearer structure. |
Day 2
| Time | Topic |
|---|---|
| 0900am - 1030am | Ethics and Responsible AI in Education Overview Participants discuss responsible AI use, bias, data risks, and human-in-the-loop practice in education. The focus is safe and thoughtful application. Learners identify what must be checked before AI-generated content reaches students or stakeholders. Learning Outcome - Identify key AI risks in education work. - Apply human-in-the-loop judgement when reviewing materials. - Set basic guardrails for responsible AI use. |
| 1030am - 1045am | Morning Break |
| 1045am - 1245pm | Micro-Course Development Lab Overview Participants apply AI tools to build a basic micro-course. The block brings together learning outcomes, content flow, activities, and checks for understanding. Participants work on a visible output rather than only discussing possible use cases. Learning Outcome - Build a basic micro-course using AI support. - Align outcomes, content, activities, and learning checks. - Produce a draft that can be refined after the programme. |
| 1245pm - 1400pm | Lunch & Prayer |
| 1400pm - 1530pm | Assessment and Pre/Post-Test Design Overview Participants create aligned learning checks that support the micro-course. The focus is on measuring understanding instead of generating random quiz questions. They use AI to draft, then human judgement to improve clarity and fairness. Learning Outcome - Create aligned pre/post-test items. - Review assessment quality using human judgement. - Improve questions for clarity, relevance, and fairness. |
| 1530pm - 1545pm | Evening Break |
| 1545pm - 1700pm | Showcase, Debrief, and Workflow Transfer Overview Participants share their micro-course drafts and receive structured feedback. The final debrief surfaces what changed in their design process with AI support. The programme closes with a practical workflow for future course development. Learning Outcome - Present a micro-course draft for feedback. - Identify one improvement before implementation. - Transfer the AI-supported workflow into future education design work. |
Learning Outcomes
By the end of the programme, participants will be able to:
- understand how GenAI can support education and course design
- use AI to draft and organise instructional materials
- create better course assets with less friction
- apply responsible AI thinking in educational settings
- build a micro-course workflow using multiple tools
They will walk away with:
- practical course development support ideas
- a more structured way to use AI in education work
- a stronger sense of where AI helps and where human judgment must stay in control
Design Philosophy
This programme should still feel like a learning journey, not a software demo.
The Accelerated Learning cycle is important because participants need to experience the usefulness of the tool, reflect on the quality of the output, practise with their own materials, and then debrief what changes in their workflow.
The real shift is not tool literacy alone. It is better judgment in how AI supports educational design.
Why This Is Different
| Generic AI Training | Generative AI in Education and Course Development |
|---|---|
| Broad AI curiosity | Education and course design use cases |
| Tool demos without structure | Practical workflow application |
| No clear output path | Micro-course and assessment outputs |
| AI as a novelty | AI as a work support system |