The Effect of Academic Burnout on AI Dependence: The Mediating Role of Self-Efficacy

Yalei Chen; Miao Huang; Xin Wang; Han Xu

doi:10.6918/IJOSSER.202607_9(7).0017

Authors

Yalei Chen School of Mathematics and Physics, Yancheng Institute of Technology, Yancheng 224051, China
Miao Huang School of Mathematics and Physics, Yancheng Institute of Technology, Yancheng 224051, China
Xin Wang School of Mathematics and Physics, Yancheng Institute of Technology, Yancheng 224051, China
Han Xu School of Mathematics and Physics, Yancheng Institute of Technology, Yancheng 224051, China

DOI:

https://doi.org/10.6918/IJOSSER.202607_9(7).0017

Keywords:

Academic Burnout; AI Dependence; Self-Efficacy; Mediating Effect; College Students.

Abstract

AI tools have become part of ordinary university study. Students use them to locate material, clarify difficult ideas, prepare drafts, and work through assignments. This convenience is useful, but it also makes it easier to hand over parts of the learning process to a system. Drawing on 548 valid questionnaires, the present study examined the association between academic burnout and dependence on AI, with general self-efficacy considered as a possible mediator. Burnout was positively related to AI dependence and negatively related to self-efficacy, whereas self-efficacy was inversely related to AI dependence. The regression results showed that burnout remained a significant predictor of AI dependence after demographic variables and self-efficacy were controlled. Bootstrap analysis further identified a significant indirect effect through self-efficacy; this pathway accounted for 14.52% of the total effect, so the mediation was partial rather than complete. The findings suggest that problematic AI reliance cannot be addressed only through rules about technology use. Support for students who are exhausted by study, together with opportunities to regain confidence through successful independent work, may be equally important.

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References

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