Learning Objectives and Competences

When considering how the learning objectives of a course or a module are impacted by AI, and what competences students should acquire, consider both:

Are the current Learning Objectives impacted by the availability and potential use of Generative AI?
- What are the foundational skills that need to be mastered by students without the use of Generative AI?
- What skills may be automated or augmented by Generative AI; in the course as well as after the course?
- How can this be reflected in the learning objectives (if not already)?
How are relevant Generative AI competences taught, and how can those contribute to the (procedural and conceptual) competences of the student? See also the additional points below for inspiration.

Generative AI Competences

When it comes to Generative AI Competences, upon graduation all students – to varying degrees – are likely to be expected to:

Master at least a basic understanding of Data Mining and Machine Learning (incl. LLMs)
Be able to use required prompt techniques (how best to use LLMs and other generative tools; sometimes referred to as “prompt engineering”
Have domain specific insights (theories, methods and tools) into the specific areas they are trained in and will be working in
Be able to assert Critical Thinking and Evaluate Generative AI output
Ensure human alignment in use/design, incl. on human goals and values and with respect to:
- Safety, Security and Robustness
- Ethics and other human aspects (incl. XAI, fairness, …)
- Scalable Oversight