Education Domain
Research in the education domain is rapidly evolving, driven by the integration of artificial intelligence, particularly large language models (LLMs) and multimodal AI, to enhance teaching, learning, and assessment. Current efforts focus on leveraging these models for personalized learning, automated feedback, and analyzing student data (e.g., through time series analysis and emotion detection) to improve educational outcomes. This work highlights the need for responsible AI development in education, addressing ethical concerns and ensuring equitable access to these powerful technologies, while also exploring the use of other AI models such as those for visual question answering and nonverbal immediacy analysis.
Papers
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Mohammad Asadi, Vinitra Swamy, Jibril Frej, Julien Vignoud, Mirko Marras, Tanja Käser
Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation
Brett A. Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, Eddie Antonio Santos