ArticleFrontiers in psychology2026
The relationship between college students' AI technology dependence and learning burnout: a chain mediation analysis of technology acceptance and AI self-efficacy.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Workplace AI Usage, Intrinsic Motivation, and Job Burnout: Evidence from a Longitudinal Study.Behavioral sciences (Basel, Switzerland) · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
Objective: Against the backdrop of the deep integration of generative artificial intelligence into higher education, the non-adaptive use of AI learning tools among college students has become a critical risk factor affecting learning psychology and academic adaptation. Methods: Based on the Technology Acceptance Model (TAM) and Self-Efficacy Theory (SET), this study constructed a chain mediation model to examine the sequential mediating associations of technology acceptance and AI self-efficacy in the relationship between AI technology dependence and learning burnout. Results: The results show that AI technology dependence is significantly positively associated with learning burnout, technology acceptance plays a significant independent mediating role between AI technology dependence and learning burnout, AI self-efficacy plays a significant independent mediating role between AI technology dependence and learning burnout, technology acceptance and AI self-efficacy form a significant chain mediation path. Conclusion: This study clarifies the multi-level psychological transmission mechanism of AI technology dependence is related to learning burnout, enriches theoretical achievements in the fields of AI education application and learning burnout, and provides empirical basis and practical implications for universities to carry out AI literacy education, alleviate college students' learning burnout, and promote a healthy and sustainable human-computer collaborative learning model.
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