AI dependency and academic motivation in higher education: the mediating role of learned helplessness and the moderating role of AI literacy

Abstract

The growing integration of generative artificial intelligence (AI) into university education has raised concerns about the psychological consequences of sustained student reliance on AI tools. While prior research has documented behavioral and cognitive effects of AI overreliance, the psychological mechanism through which AI dependency damages academic motivation remains poorly understood. Drawing on learned helplessness theory (LHT) and self-determination theory (SDT), this study proposes and tests a moderated mediation model in which learned helplessness mediates the relationship between AI dependency and academic intrinsic motivation, with AI literacy serving as a boundary condition that attenuates this pathway. Data were collected from 457 Chinese university students via a cross-sectional survey using validated scales for all four constructs, and the model was tested using covariance-based structural equation modeling (CB-SEM). Results show that AI dependency significantly and positively predicts learned helplessness, which in turn significantly and negatively predicts intrinsic motivation. The indirect effect of AI dependency on intrinsic motivation through learned helplessness was significant, confirming the proposed mediation. AI literacy significantly moderated the AI dependency-learned helplessness pathway, such that the indirect effect was weaker among students with higher AI literacy than among those with lower AI literacy. These findings extend LHT by introducing AI-mediated academic success as a novel, non-failure antecedent of learned helplessness, enrich SDT by specifying the psychological mechanism through which AI dependency becomes need-thwarting, and reposition AI literacy as a psychologically active buffer against motivational harm. The study carries direct implications for university curricula, faculty practice, and institutional policy.