AI tool use by students in Vietnamese universities: drivers and effects on academic performance
Main Article Content
Abstract
In the context of the increasingly widespread application of Artificial Intelligence (AI) tools, particularly generative AI (GenAI) models, in higher education, most current studies have mainly focused on the factors influencing the intention to use AI. However, empirical research examining students’ actual AI tool use and its direct impact on academic performance remains relatively limited. This study investigates the effects of facilitating conditions and perceived playfulness on students’ actual AI tool use, while simultaneously assessing the direct influence of AI tool use on academic performance. Data were collected from 522 university students in Vietnam and analyzed using partial least squares structural equation modeling (PLS-SEM). The findings reveal that both facilitating conditions and perceived playfulness exert positive effects on AI tool use, which in turn has a significant positive impact on academic performance. The study contributes theoretically by shifting the analytical focus from behavioral intention to actual usage behavior, integrating both environmental and intrinsic factors, and providing empirical evidence on the mechanisms through which AI influences students’ academic performance in the context of higher education in Vietnam.
Keywords
academic performance, AI tool use, facilitating conditions, higher education, perceived playfulness.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), ep448. https://doi.org/10.30935/cedtech/13417
Azamatova, A., Bekeyeva, N., Zhaxylikova, K., Sarbassova, A., & Ilyassova, N. (2023). The effect of using Artificial Intelligence and digital learning tools based on project-based learning approach in foreign language teaching on students’ success and motivation. International Journal of Education in Mathematics, Science and Technology, 11(6), 1458–1475. https://doi.org/10.46328/ijemst.3712
Bhutoria, A. (2022). Personalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Computers and Education: Artificial Intelligence, 3, 100068. https://doi.org/10.1016/j.caeai.2022.100068
Bilquise, G., Ibrahim, S., & Salhieh, S. M. (2024). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies, 29(5), 6357–6382. https://doi.org/10.1007/s10639-023-12076-x
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
Chan, C. K. Y. (2025). Understanding AI guilt: The development, pilot-testing, and validation of an instrument for students. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13629-y
Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3). https://doi.org/10.53761/1.20.3.02
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies, 29(14), 18695–18744. https://doi.org/10.1007/s10639-024-12599-x
García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the impact of Artificial Intelligence and computational sciences on student performance: Systematic review and meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171–197. https://doi.org/10.7821/naer.2023.1.1240
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
Jo, H. (2024). From concerns to benefits: A comprehensive study of ChatGPT usage in education. International Journal of Educational Technology in Higher Education, 21(1), 35. https://doi.org/10.1186/s41239-024-00471-4
Legatt, D. A. (2025). How leading universities are building the future of AI. Forbes. https://www.forbes.com/sites/avivalegatt/2025/05/15/how-leading-universities-are-building-the-future-of-ai/
Moradi, H. (2025). Integrating AI in higher education: Factors influencing ChatGPT acceptance among Chinese university EFL students. International Journal of Educational Technology in Higher Education, 22(1), 30. https://doi.org/10.1186/s41239-025-00530-4
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9
Parveen, K., Phuc, T. Q. B., Alghamdi, A. A., Hajjej, F., Obidallah, W. J., Alduraywish, Y. A., & Shafiq, M. (2024). Unraveling the dynamics of ChatGPT adoption and utilization through Structural Equation Modeling. Scientific Reports, 14(1), 23469. https://doi.org/10.1038/s41598-024-74406-4
Qiu, J. (2019). A preliminary study of English mobile learning model based on constructivism. Theory and Practice in Language Studies, 9, 1167. https://doi.org/10.17507/tpls.0909.13
Rojas, M. P., & Chiappe, A. (2024). Artificial Intelligence and digital ecosystems in education: A review. Technology, Knowledge and Learning, 29(4), 2153–2170. https://doi.org/10.1007/s10758-024-09732-7
Sharif, M. N., Zhang, L., Asif, M., Alshdaifat, S. M., & Hanaysha, J. R. (2025). Artificial intelligence and employee outcomes: Investigating the role of job insecurity and technostress in the hospitality industry. Acta Psychologica, 253, 104733. https://doi.org/10.1016/j.actpsy.2025.104733
Valle, N. N., Kilat, R. V., Lim, J., General, E., Dela Cruz, J., Colina, S. J., Batican, I., & Valle, L. (2024). Modeling learners’ behavioral intention toward using artificial intelligence in education. Social Sciences & Humanities Open, 10, 101167. https://doi.org/10.1016/j.ssaho.2024.101167
Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641–652. https://doi.org/10.1007/s10479-020-03918-9
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view1. MIS Quarterly, 27, 425–478. https://doi.org/10.2307/30036540
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
Wayne, H., Fengchun, M., & UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025