Teacher Training in Virtual-World Simulations: Analyzing Preservice Science Teacher Talk Moves With AI-Powered Student Agents

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  In Roundtable Session: Enhancing Teacher Capacities Through Technological Innovation (Table 22)

Abstract

Recent advancements in generative artificial intelligence (AI) have afforded novel developments in teacher training simulations. This study’s main objective was to observe the discourse patterns of preservice science teachers as they interacted with AI-powered student agents in a virtual-world-based teacher training simulation. A case study design was adopted, and a framework of Ambitious Science Teaching talk moves was used to analyze teacher discourse. We collected text-based discourse data from 15 preservice teachers and also transcribed in-service teacher discourse from open-source science classroom recordings for comparison. Preliminary descriptive indicate that AI-powered student agents promote ambitious teaching discourse in preservice teachers. This supports the viability of using AI-powered student agents in a teaching simulation to create an accessible teacher training tool.

Authors

• Alex Barrett, Florida State University

• Fengfeng Ke, University of Maryland

• Chih-Pu Dai, Texas A&M University

• Nuodi Zhang, Florida State University

• Saptarshi Bhowmik, Florida State University

• Luke West, Florida State University

• Xin Yuan, Florida State University

• Sherry A. Southerland, Florida State University