Speakers
Cristina Conati
University of British Columbia, CanadaStart
09/06/2023 - 09:00
End
09/06/2023 - 10:00
Keynote 5: AI-driven Personalized Support to Human Learning Beyond Problem Solving
Friday 09/06 09:00-10:00
Plenary Hall
Abstract.
Extensive evidence shows that AI-based educational technology can effectively provide personalized support to help students learn problem-solving skills in various 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 results on the application of this approach to various environments for open-ended exploratory learning.
About the speaker.
Dr. Cristina Conati is a Professor of Computer Science at the University of British Columbia, Vancouver, Canada. She received a M.Sc. in Computer Science at the University of Milan, as well as a M.Sc. and Ph.D. in Intelligent Systems at the University of Pittsburgh. Dr. Conati’s research lies at the intersection of artificial intelligence, human-computer interaction and cognitive science. Her overarching goal is to support Human-AI collaboration via trustworthy AI artifacts that can understand relevant properties of their users (e.g., states, skills, needs) and personalize the interaction accordingly in a manner that preserves transparency and user control. Cristina has been researching human-centered and AI-driven personalization for over 25 years, with contributions in the areas of Intelligent Tutoring Systems, User Modeling, Affective Computing, Information Visualization and Explainable AI.
Cristina’s research has received 10 Best Paper Awards from a variety of venues. She is an ACM Distinguished Member and AAAI Senior Member, and an associate editor for UMUAI (Journal od User Modeling and User Adapted Interaction), ACM Trabsactions on Intelligent Interactive Systems and the Journal of Artificial Intelligence in Education. She served as President of AAAC, (Association for the Advancement of Affective Computing), as well as Program or Conference Chair for several international conferences including UMAP, ACM IUI, and AI in Education.