Minds and Machines: Cognitive Science, Artificial Intelligence and Education

When: 10 Feb 2020 - 14 Feb 2020
Where: Learning Centre, University Luxembourg



Learning science as an interdisciplinary subject requires integrating distinct fields and skillsets. In particular, Cognitive Science, which studies and models the human mind, and Artificial Intelligence, which seeks to generate intelligent behavior in machines, share deep theoretical and practical concerns in the domains of education and learning which make interdisciplinary research that spans these two disciplines highly relevant. First, AI is more cognitive than appears at first glance. At the heart of the current AI revolution is a massive transfer of knowledge from humans to machines, in the form of learning from human-labeled and human-structured data. Creating and curating appropriate datasets for training AI systems requires a deep understanding of human-like knowledge representations and the subtleties of converting abstract human knowledge (e.g., what concept or skill a test question assesses) into a machine readable form. Second, AI systems most often have humans as users, as in the case of adaptive learning or assessment, requiring the AI system to maintain human interpretability. Interpretable AI requires the decisions, recommendations and advice delivered to provide sensible interpretations that can be understood by various stakeholders (such as educators, researchers or students), which imposes interesting constraints on learning methodologies for autonomous systems. Finally, Cognitive Science provides proof of concept demonstrations of learned behavior that provide next-generation targets for what AI might achieve. In this workshop we explore these themes through lectures, tutorials, and collaborative projects to enable students to participate in this exciting interdisciplinary research frontier.

By the end of this workshop, students will have gained both conceptual knowledge and practical experience in using advanced machine learning (ML) methods applied to educational settings, domains, and datasets. ML topics include: deep learning, reinforcement learning, and natural language processing; with applications to cognitive modeling and recommender systems in the educational domain.


  • Prof Dr Pedro Cardoso-Leite, Dr Dominic Mussack, University Luxembourg, Cognitive Science Group
  • Dr Siwen Guo, Prof Dr Christoph Schommer. University Luxembourg, Artificial Intelligence
  • Prof Dr Constantin Rothkopf, TU Darmstadt, Germany
  • Prof Dr Paul Schrater, University Minnesota, MN, US

For more information see the official HOMEPAGE of the Workshop.