From human to AI interactions: Mediating effects of self‐efficacy on students' engagement and well‐being in generative AI ‐assisted learning environments

Abstract Generative AI (GenAI) is reshaping the way students acquire knowledge by providing personalized and interactive learning opportunities. However, despite its growing adoption, the mechanisms through which these interactions influence students' engagement and well‐being in GenAI‐assisted learning environments (GenAI‐ALEs) remain underexplored. This study surveyed 750 university students in central China to investigate how diverse interaction forms (student – teacher, student – content, student–student and student – AI) affect their engagement and well‐being in GenAI‐ALE, mediated by multi‐dimensions of self‐efficacy: academic, social and emotional components. Partial least squares structural equation modelling (PLS‐SEM) was employed to test the hypothesized relationships, while fuzzy‐set qualitative comparative analysis (fsQCA) was used to uncover complex causal configurations, which offered a richer perspective on the underlying mechanisms. Results revealed that the multi‐dimensional self‐efficacy mediated the effects of student–student and student – AI interactions on both engagement and well‐being; academic self‐efficacy mediated the link between student – teacher interaction and these outcomes and emotional self‐efficacy mediated the relationship between student – content interaction and the same outcomes. Furthermore, the fsQCA identified multiple causal pathways that influence student engagement and well‐being. The findings yield valuable theoretical and practical implications for designing effective GenAI‐ALEs to foster students' engagement and well‐being.