Simulated Teaching and Learning at Scale

In ProgressAI in Education

Developing frameworks to evaluate AI-generated educational dialogues along two critical dimensions: simulation fidelity and interaction effectiveness.

Simulated Teaching and Learning at Scale

Overview

Developing frameworks to evaluate AI-generated educational dialogues along two critical dimensions: simulation fidelity (how realistically LLMs mimic student behavior) and interaction effectiveness (whether these exchanges produce meaningful learning). This research helps distinguish between LLMs that merely "sound like" students versus those that facilitate genuine educational progress.

Project Details

Started

2024-10

Status

In Progress

Topics

AIeducationdialogue systemsLLMsteachinglearning

Research Team

  • Kevyn Collins-ThompsonPrincipal InvestigatorUniversity of Michigan
  • Michael IonResearch Lead and Postdoctoral FellowUniversity of Michigan
  • Sumit AsthanaResearch Assistant (PhD Student)University of Michigan
  • Tianyi WangResearch Assistant (Undergraduate)University of Michigan
  • Fengquan JiaoResearch Assistant (Undergraduate)University of Michigan

Related Scholarship

Papers

  • Simulated Teaching and Learning at Scale: Balancing Fidelity and Effectiveness in Tutoring Interactions

    Michael Ion, Kevyn Collins-Thompson, S. Asthana

  • Bayesian Hierarchical Modeling of Large-Scale Math Tutoring Dialogues

    Michael Ion, Kevyn Collins-Thompson

    Joint Statistical Meetings (2025) - Under review

Talks & Presentations

  • Adaptive Knowledge Assessment in Simulated Coding Interviews

    Michael Ion, S. Asthana, Fengquan Jiao, Tianyi Wang, Kevyn Collins-Thompson

    iRAISE Workshop at AAAI Conference (2025)