Learning Activities
This is a collection of different learning activities in various formats using innovative didactic approaches to motivate students to acquire transversal skills for the use of generative AI. The activities in this toolkit are designed as flexible teaching resources and can be adapted to different disciplines, educational contexts, class sizes, and levels of study. The suggested target groups and difficulty levels should therefore be viewed as recommendations and may be adjusted to suit local learning objectives and student needs.
View the Didactic Framework as a guide to teaching and assessing AI skills.
AI Barbie, AI Bias
How culturally aware is AI? Students explore bias and representation through AI‑generated Barbies and reflect on ethical and responsible use…
AI Co-Writing
Students collaboratively write with AI, critically evaluate its contributions, and refine a structured text while reflecting on responsible AI use. …
AI Debate on the Ethics of Decision-making
Students engage in structured debates on ethical risks of AI decision-making. Based on the results, they develop evidence-based ethical guidelines. …
AI impact analysis
Stepping into the shoes of a policy maker: Students investigate the social and environmental impacts of AI and rethink guidelines. …
AI Output Assessment Lab
Students define quality criteria, evaluate AI outputs, verify information, and improve results through critical analysis and collaborative revision processes together….
AI Source-Checking Lab
Students critically evaluate AI‑generated sources: they check reliability, traceability, as well as ethical and responsible use in realistic study scenarios. …
AI-augmented Innovation Lab
From prompt to prototype: Students tackle an innovation challenge through AI-supported ideation process, problem analysis and the communication of solutions. …
AI-supported Classroom Debate
Stronger arguments with AI? Students use AI to explore perspectives, prepare debates, and reflect on critical thinking and responsible use….
BARNGA: AI Edition
What happens when you aren’t on the same wavelength as your collaborator? The game Barnga illustrates hidden challenges of human–AI…
Data Dynamics – The Fuel of AI Models
Is AI objective or shaped by data? Students experiment with datasets and discover how data drives outcomes, bias and limitations….
Foundations of AI – Concept Mapping & Reflection
From AI assumptions to understanding: Students explore beliefs, challenge myths, and build foundational understanding through reflection and a deeper exploration…
Gen AI Interview
Can you trust what AI says about itself? Students interview, analyze, and verify responses to understand how AI systems work….
Human‑AI Task‑Delegation Lab
Students break down research projects down into its constituent tasks and systematically decide how tasks should be delegated between human…
Mapping AI Support in multi-step tasks
Students break down tasks, examine how AI could support them, reflect on learning impact, and create guidelines for responsible AI…
Shared expectations
In this polling-based activity, students and instructors discuss and agree on shared expectations for the use of AI in assessments. …
Spot the Bot
Human or Machine? Students exchange texts, make assumptions about their origins, and reflect on credibility and how AI shapes academic…
The AI Verification Game
Can you trust AI? Students generate answers with AI, fact-check them, and uncover errors, building critical thinking and verification skills. …
Which for What?
Same task, different AI tools: In collaboration, students test, rank, and discuss different AI tools based on shared evaluation criteria. …
