On Monday, March 31, 2025, the Learning Informatics Lab hosted Karin de Langis from the University of Minnesota. In this talk, she discussed the current research landscape around cognition in Large Language Models (LLMs), as well as the methodological challenges involved. She also discussed her work with Minnesota NLP and highlighted several of their recent findings on LLM performance on tasks across multiple domains, including memory, executive function, and narrative comprehension.
Tag: A.I.
SmartPal: Augmenting Learning Management Systems with LLM Chatbots and Gamification with Dr. De Liu
On Friday, February 14, 2025, the Learning Informatics Lab hosted Dr. De Liu from the University of Minnesota. In this talk, Dr. Liu discussed his approach to enhancing learning engagement and performance through SmartPal, a digital learning assistant that integrates with Canvas. He also discussed the design of SmartPal, findings from a randomized field experiment on the effects of integrating AI chatbot and gamification, and highlights opportunities for research enabled by the SmartPal platform.
Leveraging Social Theories to Enhance Human-AI Interaction with Dr. Harmanpreet Kaur
On Friday, December 6, 2024, the Learning Informatics Lab hosted Dr. Harmanpreet Kaur from the University of Minnesota. In this talk, Dr. Kaur discussed her research on explainable AI and why it does not work in practice. She also shared design ideas—both completed and current work—to help people with varying expertise understand AI outputs.
Fall ’24 Colloquium: Leveraging Social Theories to Enhance Human-AI Interaction
Date: Friday, December 6
Time: 4:00 – 5:00 PM (Central Time)
Location: Education Sciences Building, Room 325
Featured Speaker: Dr. Harmanpreet Kaur (She/Her)
Assistant Professor, Department of Computer Science & Engineering
Dr. Harmanpreet Kaur is a leading researcher in human-centered artificial intelligence (AI), focusing on explainability, interpretability, and hybrid intelligence systems.
Talk OVERVIEW
As human-AI partnerships become more prevalent, their effectiveness hinges on addressing critical challenges like dynamic human needs and AI’s opaque reasoning. Many current systems fail to explain AI decision-making clearly, often perpetuating biases and overlooking nuanced edge cases.
Dr. Kaur will explore why explainable AI often falls short in practical applications and discuss innovative designs—both completed and ongoing—that aim to empower users with varying levels of expertise to better understand and interact with AI outputs.
UMN Learning Informatics Seminar with Dr. Simon Buckingham Shum (University of Technology Sydney)
On September 24, 2020, the Lab hosted a Learning Informatics Seminar featuring Dr. Simon Buckingham Shum, Professor of Learning Informatics at the University of Technology Sydney, where he serves as inaugural director of the Connected Intelligence Centre. The seminar’s title was: Learning Informatics: A.I. • Analytics • Accountability • Agency. Replay the recording below: