Paper 8: Generative Agent for Teacher Training: Designing Educational Problem-Solving Simulations with Large Language Model-based Agents for Pre-Service Teachers

Teacher training programs have often faced criticism for placing greater emphasis on theoretical knowledge at the expense of practical experiences. This often results in novice teachers who have a strong theoretical foundation but lack practical expertise. To address this issue, this study proposed "Generative Agent Design for Teacher Training." This approach utilizes a problem-solving simulation that involves GPT-4 based agents for immersive teacher training. By integrating the GPT-4 model with the widely used gaming platform Roblox, we developed more realistic educational scenarios which provide pre-service teachers with opportunities to navigate authentic teaching challenges within a controlled and safe environment. Preliminary findings, derived from interviews with three teachers who used the platform, suggest a positive response to the platform's usability. The results of this research indicate that integrating generative agents into teacher training simulation can be an effective way to offer pre-service teachers with more practical experiences to apply theories and concepts to simulated teaching practices.