Generalized AI reliance and academic procrastination among college students: the chain mediation role of academic self-efficacy and self-regulated learning

The rapid proliferation of artificial intelligence (AI) tools in higher education has profoundly reshaped students’ learning behaviors, yet their associations with academic procrastination remain underexplored. Grounded in social cognitive theory, this study proposed and tested a chain mediation model to examine how college students’ generalized reliance on AI tools is associated with academic procrastination through academic self-efficacy and self-regulated learning. A total of 729 undergraduates completed a survey using validated scales. Regression-based mediation analysis revealed that generalized AI reliance was positively associated with academic procrastination. Both academic self-efficacy and self-regulated learning served as partial mediators between generalized AI reliance and procrastination. They also formed a significant chain mediation path, accounting for a relatively modest proportion of the total association (10.25%). These findings suggest that academic self-efficacy and self-regulated learning may represent one plausible set of psychological pathways linking generalized AI reliance with academic procrastination, without establishing causal or temporal ordering. The study advances understanding of the potential dual-edged associations of AI-supported learning environments and provides practical implications that should be interpreted with appropriate caution, including the need to help students use AI tools more reflectively and strengthen self-regulatory skills in higher education.