Computational Neuroscience and Education Science

Artificial Intelligence Devoted to Education: We want to explore to what extent approaches or methods from cognitive neuroscience, linked to machine learning and knowledge representation, could help to better formalize human learning as studied in educational sciences. In other words: we are taking advantage of our better understanding of how our brains work to help us better understand how our children learn. The focus here is on learning computational thinking, i.e., what to share in terms of the skills needed to master the digital world, not just consume or endure it, considering specific learning tasks modeling.

The three operational objectives of the AIDE project

  • At the theoretical level, use the machine learning formalism as a model of the learner, as it can be done in computational neuroscience on another scale, e.g. consider reinforcement learning framework to encounter for the human learning behavior.
  • At the experimental level, use machine learning tools to obtain more formalized, more reliable, and more automated measures of variables related to a learning situation, using real life objects (e.g., unplug activity, manipulation of tangible connected objects) .
  • At the pedagogical level, help the learner to understand in his own process of learning to learn, which is a matter of mechanical processes of what is creative and making these two aspects explicit.

In other words, before using artificial intelligence to, for example, improve human learning, let’s try to use it to better understand how a human learns.

Preliminary outcomes

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