AI-driven Personalized Support to Learning Beyond Problem Solving
Pr. Cristina Conati / University of British Columbia
Abstract: There is extensive evidence that AI-based educational technology can effectively provide personalized support to help students learn problem solving skills in a variety of domains. In contrast, until recently there has been limited work on AI-based environments to support educational activities that are more exploratory in nature, such as learning from interactive simulations or playing educational games. These activities are becoming increasingly widespread, especially in the context of distant learning and other forms of self-directed learning, because they can increase motivation and foster grounded skills acquisition. However, not everyone can learn effectively from these activities, calling for AI-driven Learning Environments that can provide personalized support for open-ended exploratory learning, while interfering as little as possible with the unconstrained nature of the interaction. In this talk I will discuss the unique challenges of this endeavor, and our proposed solutions to address them, including how to devise AI-driven models that can track and react to open-ended behaviors beyond those traditionally addressed by analogous models for problem solving. I will also present our most recent results on the potential benefits of making such models explainable to their end users, especially if the explanations are personalized to each user’s specific needs.