Factors influencing students’ intention to use Artificial Intelligence (AI) for learning and research in Ho Chi Minh City

Thanh Phuc Quy Nguyen; Nhan Truong Thanh Dang

doi:10.31893/multiscience.2026669

Keywords:

intention to adopt

Artifical Intelligence in education

higher education

technology acceptance theories

Abstract

Within the context of the Fourth Industrial Revolution, the integration of artificial intelligence (AI) into higher education has become inevitable. AI tools are used by students to increase the efficiency of learning and research; however, acceptance levels vary across individuals. Among university students, usage is still mostly informal and lacks official recognition or adequate instruction, especially in developing countries. There has been limited research investigating the determinants that lead to students' intention to adopt AI in learning and research, especially within emerging economies such as Vietnam. On the basis of this gap, this study identifies and measures factors influencing the intention to use AI for learning and research among students in Ho Chi Minh City, drawing on an extended UTAUT2 model. With the extension of UTAUT2, the proposed model involves the integration of core acceptance factors with individual-level extensions. Data were collected from 258 students via an online survey and analyzed with SPSS 26. The linear regression results indicate that performance expectancy, perceived interactivity, and facilitating conditions positively and strongly predict intention to use, whereas privacy concerns exert a significant negative effeclt. The three strongest positive predictors are performance expectancy, perceived interactivity, and facilitating conditions. The findings help clarify the drivers of and barriers to AI use in higher education. Following these findings, this study has practical implications for universities, instructors, and technology developers to improve the effectiveness of AI integration in higher education. These findings are intended to reinforce learning and research outcomes and support the development of effective AI integration strategies in Vietnam’s higher education system, promoting intelligence and sustainability.
References
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23. 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
24. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363. https://doi.org/10.1016/j.im.2005.08.006
25. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Nguyen, T. P. Q., & Dang, N. T. T. (2026). Factors influencing students’ intention to use Artificial Intelligence (AI) for learning and research in Ho Chi Minh City. Multidisciplinary Science Journal, 8(10), 2026669. https://doi.org/10.31893/multiscience.2026669

[1] Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204

[2] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

[3] Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247. https://doi.org/10.1016/j.techfore.2024.123247

[4] Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8

[5] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

[6] Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA. International Journal of Human-Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495

[7] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

[8] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

[9] Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/bf02291575

[10] Kaplan, A., & Haenlein, M. (2019). Siri, Siri in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

[11] Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

[12] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

[13] Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032

[14] McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174–1182. https://doi.org/10.1016/j.artint.2007.10.009

[15] Nguyen, H. H., & Nguyen, T. K. D. (2024). Students’ intention to use artificial intelligence for learning and research: An application of the UTAUT2 model. Proceedings of the International Conference on Educational Technology and Innovation.

[16] Nguyen, T. T. T., Tran, V. A., & Le, M. H. (2024). Factors affecting the intention to use ChatGPT in study and research of students at the University of Danang. Journal of Science and Technology – University of Danang, 22(11), 1–15. https://doi.org/10.5281/zenodo.13973328

[17] Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

[18] Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989–1015. https://doi.org/10.2307/41409970

[19] Strzelecki, A. (2023). Students’ Acceptance of CHATGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education, 49(2), 223–245. https://doi.org/10.1007/s10755-023-09686-1

[20] Tiwari, C.K., Bhat, M.A., Khan, S.T., Subramaniam, R., Khan, M.A.I. (2024), "What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT". Interactive Technology and Smart Education, Vol. 21 No. 3 pp. 333–355, doi: https://doi.org/10.1108/ITSE-04-2023-0061

[21] Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x

[22] Venkatesh, N., Morris, N., Davis, N., & Davis, N. (2003). User acceptance of Information Technology: toward a unified view1. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

[23] 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

[24] Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363. https://doi.org/10.1016/j.im.2005.08.006

[25] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Abstract

References

How to cite