Pierre Dillenbourg
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Pierre Dillenbourg is full professor in learning technologies in the School of Computer & Communication Sciences at EPFL (Switzerland), where he leads the CHILI Lab: "Computer-Human Interaction for Learning & Instruction ». He graduated in educational science (University of Mons, Belgium). He started his research on learning technologies in 1984. In 1986, he applied machine learning for developing a self-improving teaching system. He obtained a PhD in computer science from the University of Lancaster (UK), in the domain of artificial intelligence applications for education. He has been senior scientist at the University of Geneva. He joined EPFL in 2002. He has been the director of Center for Research and Support on Learning and its Technologies, then academic director of Center for Digital Education, which implements the MOOC strategy of EPFL.
He has been the director of the leading house DUAL-T, which develops technologies for dual vocational education systems (carpenters, florists,...). With EPFL colleagues, he launched in 2017 the Swiss EdTech Collider, an incubator with more than 90 start-ups in learning technologies. He (co-)-founded 5 start-ups, does consulting missions in the corporate world and joined the board of several companies or institutions. In 2018, he co-founded LEARN, the EPFL Center of Learning Sciences that brings together the local initiatives in educational innovation. He is a fellow of the International Society for Learning Sciences. He currently is the Associate Vice-President for Education at EPFL as well as Vice-President for Academic Affairs (Provost) ad interim.
Pierre Dillenbourg joins the Paris IAS in May 2026 for a one-month writing residency.
Research topics
Learning techologies; learning sciences.
Methodological pitfalls in EdTech research
Empirical research on educational technologies (EdTech) generally aims to isolate the effects of an EdTech-based activity from any other variables. When other learning activities surround the Edtech-based activity or the teacher’s role is not well controlled, one would typically regard these as confounding factors. This habit betrays our latent belief that a piece of EdTech has an intrinsic effect. Consequently, even if methodological rigor establishes robust evidence, it fails to predict the effectiveness of the same activities at scale, i.e., when EdTech-based activity will be integrated with non-digital activities in a sequence orchestrated by a variety of teachers. The success of large-scale implementation depends on many factors, often placed under the magic umbrella of “the context,” some of which cannot be manipulated by an intervention. This reality often leads to a plea for qualitative methods. I instead argue that causality-preserving experimental methods could make a significant jump in predicting scaling-up effects if they factored in just two more variables: the activities before/after the EdTech-based activity and the way the teacher orchestrates the activity sequence. These two variables address the proximal context of learning activities, the classroom context, rather than the whole social, political, economic, and cultural context.
Key publications
Pierre Dillenbourg. Orchestration Graphs. EPFL Press, 2015.
Pierre Dillenbourg. "Design for classroom orchestration". Computers & Education, 69, 485-492, 2013.
Pierre Dillenbourg. "What do you mean by collaborative learning?". In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and Computational Approaches (pp.1-19). Oxford: Elsevier, 1999.
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