Practicing with AI Agents: Prospective Teachers’ Learning to Ask Questions in Mathematics

• In Event: AERA Poster Session Sunday 9:45 am
  In Poster Session: Designing Preservice Teacher Learning: Exploring Lesson Planning, Computational Thinking, AR, and Generative AI

Abstract

In response to the AERA 2026 theme, this study explores how AI can support a future vision of teacher education by developing preservice teachers’ (PSTs) responsive teaching practices to foster inclusive mathematics classrooms. Seventy-eight elementary PSTs interacted with two AI-based conversational agents, each simulating a child with distinct conceptions of fractions. Using quantitative content analysis, we examined their questioning patterns in how the PSTs interacted with the agents. Differences in questioning patterns across the two agents suggest that scenario design influences how PSTs engage. By investigating how PSTs learn to respond to AI-simulated children, this project contributes to a broader vision of teacher education that leverages emerging technologies to rehearse practice and to imagine more inclusive mathematics classrooms.

Authors

• Dabae Lee, Kennesaw State University

• Corey M. Webel, University of Missouri

• Sheunghyun Yeo, Daegu National University of Education

• Burcu Alapala, University of Missouri

• Rebekah Hanak, University of Missouri