Education faces a dual challenge with AI: on the one hand, how to teach AI and computational thinking at scale to train the experts, scientists and citizens that will shape our future; on the other hand, how to integrate AI into teaching and learning practices so that it empowers rather than threaten educators and deskill learners.
This project brings researchers in Human-Centric Design (led by Michel Beaudouin-Lafon) and in adaptive learning (led by Jill-Jênn Vie) together with practioners of teaching large computational classes and deploying tooling and infrastructure (led by Nicolas Thiéry) to explore this dual challenge and its many ramifications. The core strategy will be to setup a tight agile virtuous loop between research, technology and teaching practice: observe and evaluate the needs and practices of both teachers and learners, leverage existing and develop new methodology and technology, deploy on the battle field (leaning on Work Package XXX), evaluate, and inform further research and technology developments.
Humans first: The project will foster a human-centered approach. The scientific challenge is to create digital tools and methods that create true human-computer partnerships where users harness the power of AI and interact with it, rather than be subjected to its outputs.
User control, data privacy and governance, sustainability, and environmental impact will be guiding principles of the project.
We want to create joyful environments for both teachers and learners, fostering fairness, inclusivity, and collaboration.
Digital commons: a key asset of this project --- and in general for teaching computational thinking at scale --- is the availability of large ecosystems of standards, open source software and services developed e.g. by the Jupyter community for computing with the human in the loop. All the developments will be pursued in tight collaboration with these communities and all outcomes contributed back.
Research objectives: The project will develop adaptive learning methods through learning analytics and reinforcement learning; student and teacher models and semantic knowledge graphs; use of large language models; collaboration, authoring and grading tools for teachers; environments for interactive learning. We will explore, test, deploy and evaluate methodologies, technologies and best practices, in particular toward scaling existing courses. Finally, we will disseminate the outcomes and foster adoption, in Saclay and beyond.
The work will be conducted in collaboration with international partners such as the KWARC research group at FAU Erlangen-Nürnberg (Germany), the Crowd Research Initiative at Stanford, the NYU-LEARN lab at New York University, and the the Kashima Lab at Kyoto University.
Outcomes will include publications, contributions to existing tools, new tools, training material, and best practices. Success will be measured by evaluation of the relevance and impact on learning (user studies at different scales), publications, adoption of tools, training material and best practices, and assessment of environmental impact.
Budget: the budget (~600k€) aims to hire personnel and purchase other goods and services (travel, communication, organization of events, subcontracting for technical developments).