ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Human-Media Interaction
This article is part of the Research TopicArtificial Intelligence for Technology Enhanced LearningView all 19 articles
Exploring the Formation of Learning Burnout Among College Students in AI Context: A Serial Mediation Mechanism of AI Dependence and Addiction Based on I-PACE Model
Provisionally accepted
- School of Film Television and Communication, Xiamen University of Technology, Xiamen, China
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The rapid evolution of artificial intelligence (AI) has introduced unprecedented conveniences to college students through AI-assisted learning. However, concerns are growing regarding potential dependency on and addiction to AI technologies, which have emerged as critical challenges in higher education. To investigate the formation mechanism of learning burnout among college students in the AI context, this study proposes a serial mediation model to elucidate the pathways linking AI dependence, AI addiction, and learning burnout. The research aims to: (1) examine the effects of perceived usefulness of AI, inert thinking, and perceived enjoyment of AI on college students' AI dependence and AI addiction; (2) assess the mediating roles of AI dependence and AI addiction in the development of learning burnout; and (3) investigate the serial mediating effects of AI dependence and AI addiction between individual characteristics and learning burnout. Data were collected via questionnaire surveys from 412 Chinese college students and analyzed using structural equation modeling (SEM) and mediation analyzes with SmartPLS and SPSS. The results revealed that: (1) perceived usefulness, inert thinking, and perceived enjoyment significantly positively predicted both AI dependence and AI addiction; (2) AI dependence and AI addiction were significant predictors of learning burnout; and (3) AI dependence and AI addiction exhibited a significant serial mediating effect between perceived usefulness, perceived enjoyment, and learning burnout, whereas inert thinking influenced learning burnout only through AI dependence. This study delineates the formation mechanism from technological affordances to psychological dependence and subsequently to learning fatigue in AI-assisted learning contexts, while extending the application of the I-PACE model to higher education research. It offers novel theoretical perspectives and practical implications for mitigating AI-induced learning burnout and optimizing learning experiences. Enhancing students' self-regulatory capacities and AI literacy may effectively alleviate the risk of learning burnout associated with AI dependence and contribute to the development of a healthy, intelligent educational ecosystem.
Keywords: AI addiction, AI Dependence, Inert thinking, I-PACE model, learning burnout
Received: 28 Nov 2025; Accepted: 31 Jan 2026.
Copyright: © 2026 Lan, Liu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hao Chen
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