GPTeach: Interactive TA Training with GPT-based Students - Markel, Julia Mae; Opferman, Steven G.; Landay, James A.; Piech, Chris (EdArXiv, 2023)
Interactive and realistic teacher training is hard to scale. This is a key issue for learning at scale, as inadequate preparation can negatively impact both students and teachers. What if we could make the teacher training experience more engaging and, as a downstream effect, reduce the potential for harm that teachers-in-training could inflict on students? We present GPTeach, an interactive chat-based teacher training tool that allows novice teachers to practice with simulated students. We performed two studies to evaluate GPTeach: one think-aloud study and one A/B test between our tool and a baseline. Participants took the role of a teaching assistant conducting office hours with two GPT-simulated students. We found that our tool provides the opportunity for teachers to get valuable teaching practice without the pressures of affecting real students, allowing them to iterate their responses both during and across sessions. Additionally, participants enjoyed flexibility in tailoring their responses according to the varied personas, needs, and learning goals. In this paper, we provide quantitative results and qualitative observations to inform future work in this area. We conclude with a discussion of actionable design ideas for such systems, as well as other ways to use this tool for evaluating teachers and students. GPTeach has recently been deployed into the teacher training component of an online course with over 800 novice teachers.