AI Applications in University Teaching: Impacts on Pedagogical Practices and Learning Outcomes in Vietnam
Keywords:
Artificial intelligence (AI); higher education; PLS-SEM; pedagogical practices; learning outcomes; Vietnam; technology acceptance model (TAM); technology readiness index (TRI)Abstract
In the context of rapid digital transformation, this study explores the multidimensional impacts of artificial intelligence (AI) on higher education in Vietnam. Grounded in an integrated theoretical framework combining the technology acceptance model (TAM) and the technology readiness index (TRI), the research employs a quantitative approach using partial least squares structural equation modeling (PLS-SEM) to analyze data from 630 participants (400 students and 230 lecturers) at major universities in Hanoi and Ho Chi Minh City. The empirical results indicate that technology readiness, particularly optimism and innovativeness, serves as a critical antecedent shaping users' perceptions of AI’s usefulness and ease of use. Multi-group analysis reveals significant differences in the behavioral mechanisms between the two groups. For lecturers, AI competence and confidence in digital skills are decisive factors driving substantial innovation in pedagogical practices and assessment methods. In contrast, for students, the intention to use AI contributes to significantly improved learning outcomes through enhanced personalization, though it simultaneously raises latent challenges related to social isolation and reduced human-to-human interaction. Based on these empirical findings, the study proposes critical managerial implications for policymakers and university leaders. It emphasizes the urgent need to shift from basic tool training to comprehensive digital pedagogical competence, and advocates for the development of "human-centric" AI integration strategies that carefully balance algorithmic efficiency with the preservation of human connection, thereby ensuring sustainable and holistic educational quality.
https://doi.org/10.26803/ijlter.25.3.4
References
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in m-learning context: A systematic review. Computers & Education, 125, 389–412. https://doi.org/10.1016/j.compedu.2018.06.012
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gaševi?, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. https://doi.org/10.1016/j.caeai.2024.100215
Ambele, R. M., Kaijage, S. F., Dida, M. A., Trojer, L., & Kyando, N. M. (2022). A review of the development trend of personalized learning technologies and its applications. International Journal of Advances in Scientific Research and Engineering, 8(11), 75–91. https://doi.org/10.31695/ijasre.2022.8.11.9
Anh, N. T. P., & Ghi, T. N. (2025). Intention to adopt ChatGPT and learning outcomes of Business English students: An analysis using the TAM model and constructivist learning theory. Hung Vuong University Journal of Science and Technology, 11(1), 62–73. https://doi.org/10.59775/1859-3968.254
Banihashem, S. K., Noroozi, O., Van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, Article 100489. https://doi.org/10.1016/j.edurev.2022.100489
Chatterjee, P., Gantait, A., Swamy, G. A., & George, B. (2023). Information and communication technologies in education: A framework for transforming the Indian education system through smart learning. In Digital technologies for smart business, economics and education (pp. 235–253). Springer. https://doi.org/10.1007/978-3-031-24101-7_16
Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Singh Jagdeep, S. J. S. (2024). Analyzing preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3), Article e25896. https://doi.org/10.1016/j.heliyon.2024.e25896
Crawford, J., Allen, K. A., Pani, B., & Cowling, M. (2024). When artificial intelligence substitutes humans in higher education: The cost of loneliness, student success, and retention. Studies in Higher Education, 49(5), 883–897. https://doi.org/10.1080/03075079.2024.2326956
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
Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., & Spirgi, L. (2024). First-year students AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education, 21(1), Article 18. https://doi.org/10.1186/s41239-024-00452-7
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), Article 12. https://doi.org/10.1186/s40561-023-00231-3
Doan, H. N., Nguyen, X. B., & Vu, T. H. H. (2024). Harnessing artificial intelligence with higher education in Vietnam: Opportunities, challenges, and recommendations for legal undergraduate education. Conhecimento & Diversidade, 16(43), 621–641. https://doi.org/10.18316/rcd.v16i43.11986
Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), Article 3056. https://doi.org/10.3390/app13053056
Du, H., Sun, Y., Jiang, H., Islam, A. Y. M. A., & Gu, X. (2024). Exploring the effects of AI literacy in teacher learning: An empirical study. Humanities and Social Sciences Communications, 11(1), Article 559. https://doi.org/10.1057/s41599-024-03101-6
Elsayed, A. M., Kholikov, A., Abdullayeva, I., Al-Farouni, M., & Wodajo, M. R. (2024). Teacher support in AI-assisted exams: An experimental study to inspect the effects on demotivation, anxiety management in exams, L2 learning experience, and academic success. Language Testing in Asia, 14(1), Article 53. https://doi.org/10.1186/s40468-024-00328-7
Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), Article 1109. https://doi.org/10.3390/educsci13111109
Godoe, P., & Johansen, T. S. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3(1), 38–52. https://doi.org/10.5334/jeps.ia
Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), Article 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F., Astrachan, C. B., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2021). Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, 12(3), Article 100392. https://doi.org/10.1016/j.jfbs.2020.100392
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
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
Hashim, S., Omar, M. K., Jalil, H. A., & Mohd Sharef, N. (2022). Trends on technologies and artificial intelligence in education for personalized learning: Systematic literature review. International Journal of Academic Research in Progressive Education and Development, 11(1), 884–903. https://doi.org/10.6007/IJARPED/v11-i1/12230
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, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Joshi, M. A. (2024). Adaptive learning through artificial intelligence. International Journal on Integrated Education, 7(2), 41–43. https://doi.org/10.2139/ssrn.4514887
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), Article 56. https://doi.org/10.1186/s41239-023-00426-1
Lang, C., Wise, A. F., Merceron, A., Gaševi?, D., & Siemens, G. (2022). What is learning analytics. In The handbook of learning analytics (2nd ed., pp. 8–18). Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla22.001
Lee, D., Kim, H.-H., & Sung, S.-H. (2023). Development research on an AI English learning support system to facilitate learner-generated-context-based learning. Educational Technology Research and Development, 71(2), 629–666. https://doi.org/10.1007/s11423-022-10172-2
Lin, J. S. C., & Hsieh, P. L. (2007). The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Computers in Human Behavior, 23(3), 1597–1615. https://doi.org/10.1016/j.chb.2004.11.009
Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51, Article 101421. https://doi.org/10.1016/j.tsc.2023.101421
Lünich, M., Keller, B., & Marcinkowski, F. (2024). Diverging perceptions of artificial intelligence in higher education: A comparison of student and public assessments on risks and damages of academic performance prediction in Germany. Computers and Education: Artificial Intelligence, 7, Article 100305. https://doi.org/10.1016/j.caeai.2024.100305
Menéndez, M. H. D., Morales-Menendez, R., Escobar, C. A., & Ramirez-Mendoza, R. A. (2022). Learning analytics: State of the art. International Journal on Interactive Design and Manufacturing, 16(2), 1209–1230. https://doi.org/10.1007/s12008-022-00930-0
Ministry of Education and Training (MOET). (2025). Integrating AI into education faster and stronger. Ministry of Education and Training Electronic Portal. https://moet.gov.vn/tintuc/Pages/tin-tong-hop.aspx?ItemID=10390
Mnguni, L., Nuangchalerm, P., Zaky El Islami, R. A., Sibanda, D., Sari, I. J., & Ramulumo, M. (2024). The behavioural intentions for integrating artificial intelligence in science teaching among pre-service science teachers in South Africa and Thailand. Computers and Education: Artificial Intelligence, 7, Article 100334. https://doi.org/10.1016/j.caeai.2024.100334
Mullan, F., Mather, H., Bateman, H., Cairns, A., Nasseripour, M., Binnie, V., Dawson, L., McCracken, G., & Ellis, J. (2024). Recognising and supporting authentic learning in a changing world: The opportunities and threats of AI. British Dental Journal, 237(8), 659–662. https://doi.org/10.1038/s41415-024-7948-9
Neves, C., Oliveira, T., Cruz-Jesus, F., & Venkatesh, V. (2025). Extending the unified theory of acceptance and use of technology for sustainable technologies context. International Journal of Information Management, 80, Article 102838. https://doi.org/10.1016/j.ijinfomgt.2024.102838
Nguyen, L. T., Kanjug, I., Lowatcharin, G., Manakul, T., Poonpon, K., Sarakorn, W., Somabut, A., Srisawasdi, N., Traiyarach, S., & Tuamsuk, K. (2022). How teachers manage their classroom in the digital learning environment – experiences from the University Smart Learning Project. Heliyon, 8(10), Article e10817. https://doi.org/10.1016/j.heliyon.2022.e10817
Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
Polakova, P., & Klimova, B. (2024). Implementation of AI-driven technology into education: A pilot study on the use of chatbots in foreign language learning. Cogent Education, 11(1), Article 2355385. https://doi.org/10.1080/2331186X.2024.2355385
Pratama, M. P., Sampelolo, R., & Lura, H. (2023). Revolutionizing education: Harnessing the power of artificial intelligence for personalized learning. Klasikal: Journal of Education, Language Teaching and Science, 5(2), 350–357. https://doi.org/10.52208/klasikal.v5i2.877
Qureshi, I., Raza, H., & Whitty, M. (2021). Technology readiness and technology acceptance in e-learning: Evidence from higher education. Computers & Education, 173, Article 104271. https://doi.org/10.1016/j.compedu.2021.104271
Quy, V. K., Thanh, B. T., Chehri, A., Linh, D. M., & Tuan, D. A. (2023). AI and digital transformation in higher education: Vision and approach of a specific university in Vietnam. Sustainability, 15(14), Article 11093. https://doi.org/10.3390/su151411093
Rafiq, S., Iqbal, S., & Afzal, A. (2024). The impact of digital tools and online learning platforms on higher education learning outcomes. Al-Mahdi Research Journal, 5(4), 359–369. https://doi.org/10.5281/zenodo.10682228
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), Article 2293431. https://doi.org/10.1080/2331186X.2023.2293431
Sabiroh, M. S. S., Ismail, I., Annuar, N., Abdul Rahman, N. R., Abd Hamid, N. Z., & Abd Mutalib, H. (2024). A conceptual analysis of technology integration in classroom instruction towards enhancing student engagement and learning outcomes. International Journal of Education, Psychology and Counseling, 9(55), 750–769. https://doi.org/10.35631/ijepc.955051
Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8), Article e29523. https://doi.org/10.1016/j.heliyon.2024.e29523
Silva, G., Godwin, G., & Jayanagara, O. (2024). The impact of AI on personalized learning and educational analytics. International Transactions on Education Technology (ITEE), 3(1), 36–46. https://doi.org/10.33050/itee.v3i1.669
Tabuenca, B., Uche-Soria, M., Greller, W., Hernández-Leo, D., Balcells-Falgueras, P., Gloor, P., & Garbajosa, J. (2024). Greening smart learning environments with artificial intelligence of things. Internet of Things, 25, Article 101051. https://doi.org/10.1016/j.iot.2023.101051
Tan, K., Pang, T., Fan, C., & Yu, S. (2023). Towards applying powerful large AI models in classroom teaching: Opportunities, challenges and prospects. arXiv. https://doi.org/10.48550/arXiv.2305.03433
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215. https://doi.org/10.1016/j.im.2006.12.005
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, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167
Xu, J., Li, J., & Yang, J. (2024). Self-regulated learning strategies, self-efficacy, and learning engagement of EFL students in smart classrooms: A structural equation modeling analysis. System, 125, Article 103451. https://doi.org/10.1016/j.system.2024.103451
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Tan Dat Truong, Quoc Giang Tran
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published by IJLTER are licensed under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CCBY-NC-ND4.0).
