Generative AI in Vietnamese undergraduates: Cognitive offloading and AI dependency in critical thinking disposition
Generative artificial intelligence (AI) tools are now commonly used in undergraduate academic work, and a growing body of evidence associates their use with lower self-reported critical thinking. This study examines whether that association runs through one cognitive mechanism or two with different origins, modeling cognitive offloading and AI dependency as parallel mediators. Data were collected from a single cross-sectional survey of a convenience sample of 448 undergraduates at five institutions in the northern area of Ho Chi Minh City, conducted when none of the institutions had issued their own rules on generative AI use. Analyses used partial least squares structural equation modeling with 10,000 bootstrap samples, two one-sided equivalence tests, and a specification test against a linear benchmark. Critical thinking was measured as a self-reported disposition, not as ability on a performance test. Both mediators are on average associated with lower critical thinking disposition to a comparable degree (−0.277 and −0.240), but their origins differ. Use frequency transmits 95% of its indirect effect through offloading and shows no substantive direct association with dependency once the remaining antecedents are controlled, whereas trust in AI transmits only 7% through offloading (descriptive point-estimate shares without intervals). The second-order contrast between the two antecedents is −0.126 (95% confidence interval [−0.191, −0.070]), establishing a double dissociation. Because the two mechanisms are associated with the outcome to a comparable degree but have different origins, intervention implications cannot be read off effect magnitude: on this reading, interventions that act on the level of use would reach only one channel. The two no-association conclusions (trust to offloading, use frequency to dependency) rest on equivalence tests with power of 0.67 and 0.69 at n = 448 and need replication with n > 600.
Amoozadeh, M., Daniels, D., Nam, D., et al. (2024). Trust in generative AI among students: An exploratory study. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 67–73). ACM. https://doi.org/10.1145/3626252.3630842
Arthur, W., Jr., Bennett, W., Jr., Stanush, P. L., & McNelly, T. L. (1998). Factors that influence skill decay and retention: A quantitative review and analysis. Human Performance, 11(1), 57–101. https://doi.org/10.1207/s15327043hup1101_3
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
Bedewy, D., & Gabriel, A. (2015). Examining perceptions of academic stress and its sources among university students: The Perception of Academic Stress Scale. Health Psychology Open, 2(2). https://doi.org/10.1177/2055102915596714
Bravo, M. J., Galiana, L., Rodrigo, M. F., Navarro-Pérez, J. J., & Oliver, A. (2020). An adaptation of the Critical Thinking Disposition Scale in Spanish youth. Thinking Skills and Creativity, 38, 100748. https://doi.org/10.1016/j.tsc.2020.100748
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 188. https://doi.org/10.1145/3449287
Casner, S. M., Geven, R. W., Recker, M. P., & Schooler, J. W. (2014). The retention of manual flying skills in the automated cockpit. Human Factors, 56(8), 1506–1516. https://doi.org/10.1177/0018720814535628
Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296–325. https://doi.org/10.1177/1094428107300343
Chin, W. W., & Dibbern, J. (2010). An introduction to a permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 171–193). Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-540-32827-8_8
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Cong-Lem, N., Vu, H., & Duong, T. T. H. (2026). Translanguaging in generative AI prompting among Vietnamese EFL learners: An exploratory mixed-methods study. The Language Learning Journal, 1–13. https://doi.org/10.1080/09571736.2025.2612180
Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2024). University policies on generative AI in Asia: Promising practices, gaps, and future directions. Journal of Asian Public Policy, 18(2), 260–281. https://doi.org/10.1080/17516234.2024.2379070
Facione, P. A. (2000). The disposition toward critical thinking: Its character, measurement, and relationship to critical thinking skill. Informal Logic, 20(1), 61–84. https://doi.org/10.22329/il.v20i1.2254
Fan, W., Cheng, L., Wang, Y., Zhao, Q., & Li, Y. (2026). In-class AI use and attitudes among university students: The different mediating roles of cognitive relief and cognitive offloading. Behavioral Sciences, 16(6), 1014. https://doi.org/10.3390/bs16061014
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431. https://doi.org/10.1108/IMR-09-2014-0304
Jiang, Y., & Chen, H. (2026). AI-based cognitive offloading by students in higher education in China. Computers in Human Behavior: Artificial Humans, 8, 100327. https://doi.org/10.1016/j.chbah.2026.100327
Kalimira, A. M., & Jazayeri, K. (2026). From social drivers to sustainable AI usage and dependency in higher education: Roles of trust, perceived competence, and perceived intelligence. Sustainability, 18(3), 1598. https://doi.org/10.3390/su18031598
Kember, D. (2004). Interpreting student workload and the factors which shape students’ perceptions of their workload. Studies in Higher Education, 29(2), 165–184. https://doi.org/10.1080/0307507042000190778
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Kulal, A. (2025). Cognitive risks of AI: Literacy, trust, and critical thinking. Journal of Computer Information Systems, 1–13. https://doi.org/10.1080/08874417.2025.2582050
Lakens, D. (2017). Equivalence tests: A practical primer for t tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355–362. https://doi.org/10.1177/1948550617697177
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963
LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction, habit, or deficient self-regulation? Media Psychology, 5(3), 225–253. https://doi.org/10.1207/S1532785XMEP0503_01
Lee, J. D., & Moray, N. (1994). Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies, 40(1), 153–184. https://doi.org/10.1006/ijhc.1994.1007
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Li, C., Cui, H., & Hagedorn, L. S. (2026). The cognitive impact of ChatGPT in higher education: A systematic review of critical and creative thinking outcomes. Computers and Education: Artificial Intelligence, 10, 100571. https://doi.org/10.1016/j.caeai.2026.100571
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121. https://doi.org/10.1037/0021-9010.86.1.114
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23
Ministry of Education and Training. (2026). Circular No. 49/2026/TT-BGDĐT of 30 June 2026 on the application of technology in higher education and vocational education. Socialist Republic of Viet Nam.
Morales-García, W. C., Sairitupa-Sánchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9, 1323898. https://doi.org/10.3389/feduc.2024.1323898
Nitzl, C., Roldán, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/IMDS-07-2015-0302
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
Perkins, D. N., Jay, E., & Tishman, S. (1993). Beyond abilities: A dispositional theory of thinking. Merrill-Palmer Quarterly, 39(1), 1–21. https://www.jstor.org/stable/23087298
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Scaife, M., & Rogers, Y. (1996). External cognition: How do graphical representations work? International Journal of Human-Computer Studies, 45(2), 185–213. https://doi.org/10.1006/ijhc.1996.0048
Sosu, E. M. (2013). The development and psychometric validation of a Critical Thinking Disposition Scale. Thinking Skills and Creativity, 9, 107–119. https://doi.org/10.1016/j.tsc.2012.09.002
Su, M., Wijaya, T. T., Miričić, M., Anđić, B., & Lavicza, Z. (2025). Understanding AI dependency among pre-service teachers: A UTAUT2-based analysis using PLS-SEM and fsQCA. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2025.2604648
Tian, J., & Zhang, R. (2025). Learners’ AI dependence and critical thinking: The psychological mechanism of fatigue and the social buffering role of AI literacy. Acta Psychologica, 260, 105725. https://doi.org/10.1016/j.actpsy.2025.105725
Truong, T.-K. H. V., & Le, D. V. G. (2025). Influencing factors on AI use intention of undergraduates in Can Tho City. In 2025 International Conference on Applied Artificial Intelligence, Data Engineering and Sciences (pp. 1–5). IEEE. https://doi.org/10.1109/ICAIDES67265.2025.11404034
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Wang, H., & Tian, J. (2026). From trust to dependency: How generative AI attributes and organisational support shape student use of generative AI. Education and Information Technologies, 31(12), 4303–4326. https://doi.org/10.1007/s10639-026-13983-5
Wang, J. (2026). Cognitive offloading through digital tools and its relationship with critical thinking, task persistence, and learning depth. Frontiers in Psychology, 17, 1781101. https://doi.org/10.3389/fpsyg.2026.1781101
Wang, W., Wu, Y., Fang, J., Yang, C., & Wen, L. (2026). When cognitive offloading becomes dependence: How AI dependence mediates the pathway from academic stress to burnout and anxiety. BMC Psychology, 14, 1125. https://doi.org/10.1186/s40359-026-04911-w
Yockey, R. D. (2016). Validation of the short form of the Academic Procrastination Scale. Psychological Reports, 118(1), 171–179. https://doi.org/10.1177/0033294115626825
Yurt, E., & Kusci, I. (2026). Factors influencing critical thinking during AI use among university students: The mediating effects of epistemic laziness and metacognitive weakness. Current Psychology, 45(1), 67. https://doi.org/10.1007/s12144-025-08800-0
Zhang, D., Wijaya, T. T., Wang, Y., Su, M., Li, X., & Damayanti, N. W. (2025). Exploring the relationship between AI literacy, AI trust, AI dependency, and 21st century skills in preservice mathematics teachers. Scientific Reports, 15(1), 14281. https://doi.org/10.1038/s41598-025-99127-0
Zhang, H., Yang, Y., Zhang, Y., Qiu, B., & Yang, J. (2026). Generative AI dependency on programming among university students: A scale development and validation study. BMC Psychology, 14, 1245. https://doi.org/10.1186/s40359-026-05011-5
Zhang, J., Yao, Z., Kang, D., Rasiah, R., & Gao, N. (2026). A SEM-ANN analysis to examine impact of AI overreliance through a social cognitive lens: The roles of dependency, FOMO, addiction, and anxiety. BMC Psychology, 14(1), 547. https://doi.org/10.1186/s40359-026-04246-6
Zhang, L., & Xu, J. (2025). The paradox of self-efficacy and technological dependence: Unraveling generative AI’s impact on university students’ task completion. The Internet and Higher Education, 65, 100978. https://doi.org/10.1016/j.iheduc.2024.100978
Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). 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(1), 34. https://doi.org/10.1186/s41239-024-00467-0
Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257
