After several months of work, this initial phase has already produced a substantial body of shared resources. Since the launch in La Rochelle, we have been building a toolkit designed to be reused and adapted in accordance with the licence specified for each resource. Every element addresses a practical project need: How can we explore generative AI with young people? How can we assess what participants learn? And how can other teams build on what we have started?

A shared teaching methodology

We formalised our methodology around a six-phase scientific process designed to help young people adopt the mindset of researchers investigating AI. It begins with observing and documenting AI in the world around them (P1), followed by developing hypotheses through unplugged activities (P2), experimenting with language models (P3), validating ideas through robotics activities (P4), reflecting on societal and ethical issues (P5), and finally communicating and showcasing the projects developed by young people during the first five phases (P6). Each stage is linked to specific competences from our skills tree, examples of adaptable activities and key considerations drawn from field experience. The document is now available in English and in all four project languages on our website: youth-ai-lab.eu.

deliverables/ComprehensiveApproach at master · Youth-AI-Lab/deliverables

The Youth AI Lab approach is structured around six complementary phases.

Activity idea sheets for designing workshops

The activity idea sheets present activities that are easy to understand, adapt and test with different audiences. They will gradually be supplemented by more detailed protocol sheets enriched with feedback from the field.

The activity idea sheets are available in English. On the wiki, automatic translation will make them accessible in all the project languages. Protocol sheets, meanwhile, will be provided directly in the local language in which each activity is implemented. Activities for the later phases will be added as they are published.

The activity idea sheets currently available are listed below according to the different phases of the learning journey.

Observe AI in the world around you

• Keep an Eye on AI: An easy-to-adapt field investigation that helps a group identify AI in its surroundings and distinguish artificial intelligence from automation.
• AI Lab - Inside the Machine: Three screen-free games that use hands-on experience to show how a machine identifies patterns and makes predictions.
• Looks Right… Is It Right?: A fact-checking activity that develops critical thinking about convincing but sometimes false AI-generated answers.
• Illusion Is All That Matters: A guided debate questioning what it means to “think” and opening a discussion about the uses of AI and robotics.

Develop hypotheses about how it works

• AI Detectives: How Do Machines Learn?: A simple hands-on exercise showing how the choice and quality of examples influence what a machine learns.
• Bio-inspired Algorithms: Reinforce Your Learnings: A playful, physical simulation for understanding reinforcement learning before programming a robot.
• Bias: Genre Matters: A practical activity that reveals a model’s gender stereotypes and teaches participants how to observe, measure and discuss them.