ArticleBehavioral sciences (Basel, Switzerland)2025
Effect of GenAI Dependency on University Students' Academic Achievement: The Mediating Role of Self-Efficacy and Moderating Role of Perceived Teacher Caring.
Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-assisted history education in China: a secondary evidence map of psychological antecedents and learning outcomes.Frontiers in psychology · 2026Pooled it
- From Critical Thinking to Passive Acceptance: The Moderating Role of Decision Fatigue in Students' Use of Generative AI.European journal of investigation in health, psychology and education · 2026Article
- Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review.Behavioral sciences (Basel, Switzerland) · 2026Review
- Generative AI dependency on programming among university students: a scale development and validation study.BMC psychology · 2026Article
- Guideline-based clinical reasoning in periodontology education: a comparative study of residents and large language models.BMC medical education · 2026Article
- Supportive University Climate and AI Dependence Among Undergraduates: The Sequential Mediating Role of Academic Self-Efficacy and Self-Control.Behavioral sciences (Basel, Switzerland) · 2026Article
- How Generative AI Use Styles Shape Academic Engagement: The Roles of Academic Impostor Syndrome and AI Policy Clarity.Behavioral sciences (Basel, Switzerland) · 2026Article
- AI dependency and academic motivation in higher education: the mediating role of learned helplessness and the moderating role of AI literacy.Frontiers in psychology · 2026Article
- The relationship between academic self-efficacy and GenAI dependence among college students: a chain mediation model of academic stress and perceived usefulness of GenAI.Frontiers in psychology · 2026Article
- College students' metacognitive awareness of generative-AI reliance: an experimental study of decision confidence and attribution bias.Frontiers in psychology · 2026Article
- Undergraduate nursing students' attitudes and needs regarding the use of generative artificial intelligence in professional learning: a qualitative study.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Generative artificial intelligence (GenAI) holds significant potential to enhance university students' learning. However, over-reliance on it to complete academic tasks poses a risk to academic achievement by potentially encouraging cognitive outsourcing. Despite this growing concern and an expanding body of research on GenAI usage, the mechanisms through which GenAI dependency and perceived teacher caring affect their academic achievement and self-efficacy remain underexplored. Based on the theory of media system dependence, this study explores the mechanisms through which university students' dependency on GenAI affects their academic outcomes, focusing on the mediating role of self-efficacy and moderating role of perceived teacher caring. A survey was conducted with 418 university students from Chinese public universities who had used GenAI for an extended period. The results revealed that GenAI dependency positively predicts false self-efficacy and negatively predicts academic achievement, exhibiting a significant Dunning-Kruger effect. Perceived teacher caring moderates the relationship between GenAI dependency and self-efficacy. High perceived teacher caring mitigates the Dunning-Kruger effect but has a weak moderating effect on academic achievement. These findings enhance the explanatory power of the media system dependency theory in educational contexts and reveal the pathways through which GenAI dependency and teacher caring affect learning processes and outcomes. This study expands the theoretical implications of teacher caring in the digital age and provides empirical evidence to aid higher education administrators in optimising AI governance and teachers in improving instructional interventions.
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