Abstract
We aimed to investigate the responsive teaching practice of preservice teachers (PSTs) using an artificial intelligence-based chatbot designed to serve the dual roles of a virtual student and a virtual mentor. We identified four unique profiles of PST's questioning patterns using latent profile analysis. We examined the changes in PSTs' questioning patterns after receiving feedback from a virtual mentor and investigated the differences in their self-assessments regarding interaction effectiveness by profile. PSTs' tendency to adjust their questions varied. There was no statistical difference in the effectiveness of interactions among profiles. We include theoretical and empirical implications for teacher education.
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