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Fourth, the positive role of social influence implies that instructors and peer
environments matter. Universities should encourage faculty members to discuss AI
openly, model responsible use, and create classroom climates in which AI is treated as a
learning-support tool rather than a taboo topic or an unregulated shortcut.
Finally, the policy implications extend beyond individual institutions. In the broader
context of national digital transformation, universities need to align AI-related guidance
with larger goals of digital capacity development, digital pedagogy, and responsible
innovation in higher education [1,2].
6. Conclusion
This study examined the factors influencing university students’ intention to use
artificial intelligence tools for learning in Vietnam. Based on 210 valid observations and a
refined measurement model, the results show that all retained scales achieve acceptable
reliability, EFA confirms five independent factors with cumulative explained variance of
62.963%, and the regression model is statistically significant and explains 80.5% of the
variance in intention to use.
In terms of relative influence, perceived usefulness is the strongest determinant of
intention to use AI tools for learning, followed by AI self-efficacy, perceived ease of use,
trust in AI, and social influence. These findings indicate that AI adoption in higher
education depends not only on whether students regard AI as valuable, but also on how
easy it is to use, how confident they feel in using it, how much they trust its outputs, and
how much support or legitimacy they perceive in the surrounding academic environment.
The study contributes by offering context-specific empirical evidence for Vietnam
and by linking the issue of AI adoption to the broader digital transformation agenda in
higher education. At the same time, the study has several limitations. The data are cross-
sectional and self-reported, and therefore do not fully capture actual usage behavior over
time. In addition, the model focuses only on direct effects. Future studies should consider
more advanced analytical techniques such as CFA and SEM to test both measurement and
structural models more rigorously. They may also incorporate mediating or moderating
variables, such as attitude, digital competence, perceived risk, or academic integrity
concerns, in order to explain the underlying mechanisms of AI adoption more deeply.

References
[1]. Duong, C. D. (2024). Modeling the determinants of HEI students’ continuance
intention to use ChatGPT for learning: A stimulus–organism–response approach. Journal
of Research in Innovative Teaching & Learning, 17(2), 391–407.
https://doi.org/10.1108/JRIT-01-2024-0006
[2]. Linh, T. T. (2025). Factors influencing the use of ChatGPT in student learning in
Vietnam. International Journal of Advanced and Applied Sciences, 12(7), 76–86.
https://doi.org/10.21833/ijaas.2025.07.007
[3]. Maheshwari, G. (2024). Factors influencing students’ intention to adopt and use
ChatGPT in higher education: A study in the Vietnamese context. Education and
Information Technologies, 29(10), 12167–12195. https://doi.org/10.1007/s10639-023-
12333-z
[4]. Mukhamedkarimova, D. F., & Umurkulova, M. M. (2025). Factors contributing to
higher education students’ acceptance of artificial intelligence: A systematic review.
European Journal of Educational Research, 14(4), 1373–1388.
https://doi.org/10.12973/eu-jer.14.4.1373

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