Leveraging Network Analytics to Examine the Impact of Generative Artificial Intelligence-Assisted Feedback on Inquiry-Based Discussion.

This article investigates the impact of generative artificial intelligence (GAI)–assisted feedback, specifically via a GPT-4.0-powered chatbot, on inquiry-based discussion (IBD) processes among pre-service teachers at a Chinese university. Using a quasi-experimental design and epistemic network analysis (ENA) grounded in the Community of Inquiry (CoI) framework, the study found that GAI-assisted feedback significantly altered group interaction patterns, enhancing social and behavioral engagement and improving group report performance, though it did not notably advance higher-order thinking skills. The research also highlights that learners’ academic motivation and self-efficacy influence how they interact with the GAI chatbot, with collaboration self-efficacy showing a significant association with distinct usage patterns. The findings underscore the importance of developing AI literacy, feedback literacy, and IBD skills to effectively integrate GAI tools in educational settings and call for innovative assessment methods that capture the nuanced learning processes facilitated by such technologies.