Beyond Cognition: Designing Emotionally Intelligent Conversational Agents for the Future of Human Learning

Dr. Emmanuel Ayedoun 
is an Associate Professor at Kansai University’s Faculty of Engineering Science (Japan) and a former recipient (DC1) of the JSPS Fellowship for Young Researchers. He specializes in developping emotionally intelligent conversational agents for language learning. His research bridges artificial intelligence and educational psychology, focusing on how virtual agents can adapt to learners’ emotional states to enhance second language acquisition. His work has been published in premier venues including IEEE Transactions on Learning Technologies and the International Journal of Artificial Intelligence in Education. Currently supported by a JSPS Grant-in-Aid for Early-Career Scientists, his research investigates how emotion-aware virtual agents can be practically implemented across diverse educational contexts.
 

Abstract:  This talk explores the exciting frontier of emotionally intelligent conversational agents designed to revolutionize human learning experiences. Moving beyond traditional “cognition-focused” systems, we present ongoing research toward agents capable of recognizing, understanding, and empathetically responding to learners’ emotional states through adaptive scaffolding and culturally-sensitive communication strategies. We envision a future where emotional contagion mechanisms enable agents to positively influence learner affect, creating more engaging and sustained learning experiences. The presentation will discuss technical challenges, authoring tool development for educators, and exciting applications beyond language learning…
 

Integrating Innovations in Clinical Science and Artificial Intelligence to study the Dynamics of Therapeutic Change

Dana Atzil-Slonim 
is a professor of clinical psychology at the Psychology Department at Bar-Ilan University (BIU). She is the director of the psychotherapy research lab, the research director of the BIU clinic and the director of the University’s Impact Center for Personalized Treatment of Depression. Her research focuses on exploring the intrapersonal and interpersonal processes that occur in both client and therapist and their association with treatment outcomes. Her lab uses advanced artificial intelligence techniques with the aim of advancing precision in the treatment of depression. Prof. Atzil-Slonim received the outstanding early achievement award from the international Society for Psychotherapy Research.
 

Abstract:  Understanding what works for whom and when in psychological therapies remains a central challenge in mental health research and practice. Recent advances in AI offer unprecedented opportunities to systematically investigate this question by modeling the inherent complexity and dynamics of mental health. In this talk, I will demonstrate how integrating a top-down, theory-driven approach with bottom-up, data-driven AI methods has enabled our interdisciplinary team of clinicians and AI researchers to work closely together in uncovering the dynamics that drive therapeutic change. I will begin by outlining the key challenges in conceptualizing, treating, and researching mental health. Next, I will discuss significant theoretical shifts in clinical science—such as the transition from general treatment models to transtheoretical processes, and from one-person to two-person psychology—that have paved new pathways for addressing these challenges. Building on these foundations, I will illustrate how our team has leveraged theoretical innovations and advancements in multimodal analysis and AI to explore the dynamic processes within clients (intrapersonal dynamics) and between clients and therapists (interpersonal dynamics) that are linked to improved treatment outcomes. Finally, I will discuss the implications of these findings for clinical practice and training, emphasizing how AI-driven insights can improve diagnostic precision, inform personalized interventions and enhance the overall efficacy of mental health treatments.