Date: Wednesday, October 22, 2025
Speaker: Conrad Borchers
Title: Shaping Self-Regulated Learners in the LLM Era: Evidence, Models, and Design
Abstract: Large language models (LLMs) and other highly capable foundation models become increasingly integrated into educational practice through teacher-, student-, and administrator-facing tools. However, emerging evidence highlights a risk: users, and especially students, may overly rely on these tools, undermining effort and self-regulation. Similar concerns have been observed in earlier intelligent learning technologies, such as tutoring systems, where learners sometimes bypass cognitive effort by exploiting instructional support features. This talk examines how measurement of LLM and learner behavior can be leveraged to empirically demonstrate (or challenge) the purported advantages of advanced AI tools for education. It presents recent research on statistical methods for evaluating LLM-based instruction, process-level assessments of learning and self-regulation, and the human-centered design of tools that support student effort. The talk culminates in a call for stronger design-based inquiry into learner-LLM interactions, urging the creation of pedagogically attuned tools that scaffold effortful engagement.
Bio: Conrad Borchers is a Ph.D. student at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, School of Computer Science, advised by Vincent Aleven and Ken Koedinger. His research advances intelligent systems that support learner persistence, assessment, and educational outcomes through human-centered design and learning analytics. He holds an MSc in Social Data Science from the University of Oxford and a BSc in Psychology from the University of Tübingen.