Programme outline
Learning objectives
By the end of the course, participants will:
- Apply structured reasoning frameworks to critique AI-generated analyses, recommendations, or written outputs
- Design and document an AI-augmented workflow for a specific professional role or task
- Demonstrate verification techniques to identify unsupported claims or over-confident AI reasoning
- Articulate appropriate limits and approval checkpoints for Artificial Intelligence (AI) use in professional decision-making
Day 1
- From AI Usage to AI-Augmented Judgement
- Identifying Augmentable vs Human-Led Work
- Risks of Unstructured AI Use
- Problem Framing and Assumption Surfacing with AI
- Using AI for Alternative Viewpoints and Structured Critique
- Iterative Improvement of Professional Work Products
Day 2
- AI as Decision Support, Not Decision Maker
- Detecting Hallucinations, Weak Evidence, and Over-Confidence
- Structured Verification Techniques for AI-Assisted Outputs
- Defining Boundaries for Appropriate AI Use
- Maintaining Trust and Accountability in AI-Augmented Workflows
- Designing Guardrails for Human-in-the-Loop Collaboration
- Assessment
Assessment
- Multiple Choice Question (MCQ) test and open-ended questions