The dark side of AI in education: AI dependency as a mediator linking academic self-efficacy and teacher support to learning burnout among university students.

The rapid integration of AI technologies into higher education has reshaped students' learning experiences, yet also raised concerns regarding AI dependency and its psychological consequences. This study investigates the relationships among academic self-efficacy, teacher support, AI dependency, and learning burnout among university students, with a particular focus on the mediating role of AI dependency. A quantitative cross-sectional research design was adopted, and data were collected from university students through an online structured questionnaire survey. Statistical analyses were conducted to assess the reliability and validity of the measurement constructs and to examine the relationships among the variables. The results indicate that both academic self-efficacy and teacher support are negatively associated with learning burnout and are negatively associated with AI dependency. Furthermore, AI dependency is significantly and positively associated with higher levels of learning burnout. Mediation analysis reveals that AI dependency mediates the relationships between academic self-efficacy and learning burnout, as well as between teacher support and learning burnout. These findings deepen the understanding of the psychological processes underlying AI-assisted learning by identifying AI dependency as an important explanatory factor associated with the relationship between psychological and contextual factors and university students' learning burnout. The study also offers practical implications for educators, higher education institutions, and policymakers to promote responsible AI use and reduce learning burnout among university students.