FA-0543Agentic & Generative AIAI for Leaders & Business

Introduction to AI: Generative Tools for Learning and Development

A practical, non-programming workshop for training and knowledge-work teams

Apply generative AI to course materials, document research and everyday training tasks, with human review and responsible data handling.

Introduction

Why this course

This two-day introductory workshop explores how generative AI can support learning and development. Participants practise drafting syllabi, course learning outcomes, assessment rubrics and question banks, then review the results for accuracy, relevance and appropriate source attribution.

Hands-on activities use conversational AI, a source-grounded notebook assistant and productivity tools. Examples draw on illustrative workplace training scenarios, not a named organisation’s policies or confidential documents. The emphasis is on practical evaluation and small, manageable pilots rather than advanced model development.

Tool exercises depend on available accounts, licences and enabled features. Only approved, non-confidential or synthetic material is used in practice.

Learning outcomes

The course teaches participants to:

Distinguish AI, machine learning, deep learning and generative AI, including their practical limitations.
Write and refine prompts for syllabi, learning outcomes, rubrics, question banks and summaries.
Use source-grounded notebook workflows to query documents, create notes and check cited passages.
Evaluate AI-assisted drafting and visualisation workflows for training tasks.
Apply a generate–review–verify–attribute–publish workflow to learning content.
Identify privacy, bias, transparency and governance concerns in proposed AI use.
Outline a small departmental pilot with review responsibilities and evaluation criteria.

Prerequisites

Basic familiarity with a browser, word processor and presentation software; no programming experience is required.
A laptop with internet access and approved access to the tools used in exercises.
Willingness to test prompts, review outputs and collaborate. Prior AI experience is helpful but optional.

Training outline

2 modules

01Day 1 — Foundations, Prompting and Source-Grounded Content1 topics

1. AI Foundations

AI, machine learning, deep learning and generative AI: terminology, large language models and transformers.
Capabilities and limitations, including hallucinations and gaps in domain knowledge.
Illustrative learning-and-development use cases: content generation, summarisation, question answering, translation and knowledge bases.
Regional AI initiatives and governance considerations, with examples checked against current published information.

2. Prompting and Iterative Evaluation

Instructions, context, constraints and examples: zero-shot and few-shot prompting.
Choose prompting techniques for the tool and task; request useful explanations and evidence without treating generated reasoning as proof.
Refine prompts through comparison and output review.
Templates for syllabi, learning outcomes, rubrics, question banks and summaries.

3. Course-Content Workshop

Use ChatGPT or an approved conversational AI tool to draft a sample syllabus, learning outcomes, rubric and question bank.
Compare drafts against the learning need, correct inaccuracies and refine the prompts.
Introduce prompt chaining and retrieval-augmented generation conceptually; no production integration is assumed.

4. Document Notebook Essentials

Use a source-grounded notebook assistant such as Google’s NotebookLM/Gemini Notebook to upload approved documents and ask focused questions.
Create a small course-reference notebook with notes, summaries and cited answers.
Check cited passages against the source and record source dates separately where version tracking is needed.
Practise with public, synthetic or explicitly approved documents.

02Day 2 — Productivity, Responsible Use and Pilot Planning1 topics

5. Productivity and Visualisation Tools

Explore AI-assisted drafting, summarisation and rewriting in Microsoft 365 applications, subject to account, licence and tenant configuration.
Create and review slide outlines and training visuals; use Napkin AI as an example of text-to-visual generation.
Combine reviewed notebook summaries with presentation or document workflows; do not assume automatic product-to-product integration.

6. Responsible AI and Human Review

Transparency, accountability, fairness, privacy and explainability.
Bias, hallucinations, factual verification, source attribution and copyright considerations.
Human review workflow: generate, check correctness, validate sources, refine and approve before publication.
Apply the workflow to an illustrative training assessment; retain a clear review and attribution trail.

7. Governance and Implementation Planning

Data leakage, overreliance, misuse and reputational risks.
Define review roles, approved information sources and change-management responsibilities.
Plan a small learning-and-development pilot with practical evaluation measures and monitoring.
Discuss domain-specific obstacles, constraints and governance requirements.

8. Review and Next Steps

Group presentation of an AI-assisted course component or pilot proposal.
Review key lessons, feedback and reflections.
Identify the next supervised experiment and the checks needed before wider use.

A programme built around your team.

Share your training goals and requirements.