ATLAS – AI for Teaching and Learning (AI) at Scale

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.