Evaluation of an LLM-Powered Student Agent for Teacher Training
摘要
As technology continues to advance, there is a growing interest in exploring the potential of generative agents and large language model (LLM)-powered virtual students to revolutionize the field of education. In this work, we present Evelyn AI, a LLM-powered virtual student conversation agent that we developed for pre-service teacher training in a virtual environment. Students powered by Evelyn AI exhibit varying baseline conceptual understanding levels, dynamic cognitive-affective states, and short-term memory. These features enable personalized, adaptive training and promote a more engaging and immersive learning experience for pre-service teachers. We describe the design and implementation of Evelyn AI, and report results of alpha testing to assess the utility of Evelyn AI for pre-service teacher training.