Exploring the Impact of Teacher Support on Chinese University Students' Academic Buoyancy and Academic Enjoyment in <scp>AI</scp> ‐Assisted Learning: A Mixed‐Method Study

ABSTRACT As artificial intelligence (AI) becomes increasingly integrated into educational settings, the role of teacher support in fostering students' adaptive capacities warrants greater attention. Grounded in Self‐Determination Theory (SDT), this mixed‐method study employed a quasi‐experimental design to investigate the impact of teacher support on academic buoyancy and academic enjoyment among Chinese university students in AI‐assisted classrooms. Participants were randomly assigned to an experimental group and a control group. The experimental group received structured teacher support throughout their AI‐assisted learning experience, while the control group followed standard AI‐assisted instruction without targeted support. Pre‐ and post‐intervention measures of academic buoyancy and academic enjoyment were analysed using the ANCOVA method. Results revealed that students in the experimental group showed significantly greater improvements ( p &lt; 0.001) in both academic buoyancy and academic enjoyment compared to the control group. Qualitative findings further revealed that teacher support helped humanise the AI‐assisted learning experience by offering emotional reassurance, clarifying AI feedback, and enabling effective tool use, which collectively enhanced students' confidence and motivation. These findings underscore the importance of human support in optimising the benefits of AI‐assisted university education.