AI-Supported Learning, AI Dependency, and Academic Stress as Predictors of Student Burnout
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Abstract
Generative artificial intelligence (GenAI) can enhance university learning by providing immediate feedback, clarifying complex concepts, supporting problem-solving, and improving access to personalized academic assistance. However, excessive reliance on AI may weaken independent reasoning, academic self-efficacy, and students’ ability to complete tasks without technological support. Previous studies suggest that constructive AI use may function as a learning resource, whereas AI dependency and persistent academic stress may contribute to exhaustion, disengagement, and reduced academic efficacy. Nevertheless, these factors have rarely been examined simultaneously in relation to academic burnout, particularly among Thai university students. This study therefore examined AI-supported learning, AI dependency, and academic stress as predictors of academic burnout among undergraduate students at Ubon Ratchathani University. Data were collected from June 2025 to March 2026 using a quantitative cross-sectional survey. Proportionate stratified sampling across academic fields, supplemented by quota-assisted convenience sampling, yielded 372 students with experience using GenAI for academic purposes. Data were analyzed using descriptive statistics, reliability and validity assessments, Pearson’s correlation, and multiple regression analysis. The model explained 46.0% of the variance in academic burnout. Academic stress was the strongest positive predictor, followed by AI dependency, whereas AI-supported learning had a negative but nonsignificant effect. The findings highlight the importance of promoting responsible AI use while reducing academic pressure and preventing excessive AI dependency.
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References
C. K. Y. Chan, W. Hu, Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education, International Journal of Educational Technology in Higher Education 20 (2023) 43.
J. Garzón, E. Patiño, C. Marulanda, Systematic review of artificial intelligence in education: Trends, benefits, and challenges, Multimodal Technologies and Interaction 9(8) (2025) 84.
T. D. Pham, N. Karunaratne, B. Exintaris, D. Liu, T. Lay, E. Yuriev, A. S. Lim, The impact of generative AI on health professional education: A systematic review in the context of student learning, Medical Education 59(12) (2025) 1280–1289.
Y. Qian, Pedagogical applications of generative AI in higher education: A systematic review of the field, TechTrends 69 (2025) 1105–1120.
L. Fan, K. Deng, F. Liu, Educational impacts of generative artificial intelligence on learning and performance of engineering students in China, Scientific Reports 15 (2025) 16220.
C. Zhai, S. Wibowo, L. D. Li, The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review, Smart Learning Environments 11 (2024) 28.
H. Wu, H. Ni, W. Luo, T. Wu, Development and validation of the AI dependence scale for Chinese undergraduates and a preliminary exploration, Frontiers in Psychology 16 (2026).
W. Jia, L. Pan, S. Neary, Effect of GenAI dependency on university students’ academic achievement: The mediating role of self-efficacy and moderating role of perceived teacher caring, Behavioral Sciences 15 (2025) 635.
W. B. Schaufeli, I. M. Martínez, A. Marques Pinto, M. Salanova, A. B. Bakker, Burnout and engagement in university students: A cross-national study, Journal of Cross-Cultural Psychology 33(5) (2002) 464–481.
M. M. Kaggwa, J. Kajjimu, J. Sserunkuma, S. M. Najjuka, L. M. Atim, R. Olum, A. Tagg, F. Bongomin, Prevalence of burnout among university students in low- and middle-income countries: A systematic review and meta-analysis, PLOS ONE 16(8) (2021) e0256402.
Z. Liu, Y. Xie, Z. Sun, D. Liu, H. Yin, L. Shi, Factors associated with academic burnout and its prevalence among university students: A cross-sectional study, BMC Medical Education 23 (2023) 317.
E. López-Gómez, R. González-Fernández, B. Khampirat, Psychometric study of the Maslach Burnout Inventory–Student Survey on Thai university students, Scientific Reports 15 (2025).
T. Lesener, L. S. Pleiss, B. Gusy, C. Wolter, The study demands–resources framework: An empirical introduction, International Journal of Environmental Research and Public Health 17(14) (2020) 5183.
Y.-L. Liu, Z. Cao, The impact of social support and stress on academic burnout among medical students in online learning: The mediating role of resilience, Frontiers in Public Health 10 (2022) 938132.
R. Kilic, J. Nasello, V. Melchior, J. M. Triffaux, Academic burnout among medical students: Respective importance of risk and protective factors, Public Health 198 (2021) 187–195.
Y. Rosales-Ricardo, F. Rizzo-Chunga, J. P. Mocha-Bonilla, J. P. Ferreira, Prevalence of burnout syndrome in university students: A systematic review, Salud Mental 44(2) (2021) 91–102.
J. Zhang, J. Meng, X. Wen, The relationship between stress and academic burnout in college students: Evidence from longitudinal data on indirect effects, Frontiers in Psychology 16 (2025).
A. Almassaad, H. A. Alajlan, R. Alebaikan, Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education, Systems 12(10) (2024) 385.
J. Batista, A. Mesquita, G. Carnaz, Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review, Information 15(11) (2024) 676.
D. T. K. Ng, C. W. Tan, J. K. L. Leung, Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study, British Journal of Educational Technology 55(4) (2024) 1328–1353.
X. Xu, L. Qiao, N. Cheng, H. Liu, W. Zhao, Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support, British Journal of Educational Technology 56 (2025) 1842–1863.
S. Zhang, X. Zhao, T. Zhou, J. H. Kim, Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior, International Journal of Educational Technology in Higher Education 21 (2024) 1–14.
D. H. Chang, M. P.-C. Lin, S. Hajian, Q. Q. Wang, Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization, Sustainability 15(17) (2023) 12921.
A. B. Bakker, E. Demerouti, Job demands–resources theory: Taking stock and looking forward, Journal of Occupational Health Psychology 22(3) (2017) 273–285.
M. Brand, E. Wegmann, R. Stark, A. Müller, K. Wölfling, T. W. Robbins, M. N. Potenza, The Interaction of Person–Affect–Cognition–Execution model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors, Neuroscience & Biobehavioral Reviews 104 (2019) 1–10.
C. Gu, B. He, X. Zhang, The relationship between college students’ AI technology dependence and learning burnout: A chain mediation analysis of technology acceptance and AI self-efficacy, Frontiers in Psychology 17 (2026).
W. Wang, Y. Wu, J. Fang, C. Yang, L. Wen, When cognitive offloading becomes dependence: How AI dependence mediates the pathway from academic stress to burnout and anxiety, BMC Psychology (2026).
M. C. Pascoe, S. E. Hetrick, A. G. Parker, The impact of stress on students in secondary school and higher education, International Journal of Adolescence and Youth 25(1) (2020) 104–112.
R. S. Lazarus, S. Folkman, Stress, appraisal, and coping, Springer (1984).
M. S. Setia, Methodology series module 3: Cross-sectional studies, Indian Journal of Dermatology 61(3) (2016) 261–264.
X. Wang, Z. Cheng, Cross-sectional studies: Strengths, weaknesses, and recommendations, Chest 158(1S) (2020) S65–S71.
I. Etikan, K. Bala, Sampling and sampling methods, Biometrics & Biostatistics International Journal 5(6) (2017) 215–217.
G. Sharma, Pros and cons of different sampling techniques, International Journal of Applied Research 3(7) (2017) 749–752.
F. Faul, E. Erdfelder, A. Buchner, A.-G. Lang, Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses, Behavior Research Methods 41(4) (2009) 1149–1160.
A. Joshi, S. Kale, S. Chandel, D. K. Pal, Likert scale: Explored and explained, British Journal of Applied Science & Technology 7(4) (2015) 396–403.
D. Bedewy, A. Gabriel, Examining perceptions of academic stress and its sources among university students: The Perception of Academic Stress Scale, Health Psychology Open 2(2) (2015) 1–9.
M. A. Hertzog, Considerations in determining sample size for pilot studies, Research in Nursing & Health 31(2) (2008) 180–191.
T. J. Dunn, T. Baguley, V. Brunsden, From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation, British Journal of Psychology 105(3) (2014) 399–412.
A. F. Hayes, J. J. Coutts, Use omega rather than Cronbach’s alpha for estimating reliability, Communication Methods and Measures 14(1) (2020) 1–24.
J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, Multivariate data analysis, 8th ed., Cengage (2019).
J. H. Kim, Multicollinearity and misleading statistical results, Korean Journal of Anesthesiology 72(6) (2019) 558–569.
P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee, N. P. Podsakoff, Common method biases in behavioral research: A critical review of the literature and recommended remedies, Journal of Applied Psychology 88(5) (2003) 879–903.
P. M. Podsakoff, S. B. MacKenzie, N. P. Podsakoff, Sources of method bias in social science research and recommendations on how to control it, Annual Review of Psychology 63 (2012) 539–569.
J. Shi, W. Liu, K. Hu, Exploring how AI literacy and self-regulated learning relate to student writing performance and well-being in generative AI-supported higher education, Behavioral Sciences 15 (2025).
L. Zhang, J. Xu, The paradox of self-efficacy and technological dependence: Unraveling generative AI’s impact on university students’ task completion, The Internet and Higher Education 65 (2024) 100978.
