University students’ intentions to use AI applications for learning an additional foreign language:
A TAM study in Vietnam
DOI:
https://doi.org/10.60087/ijls.vol3.n3.004Keywords:
artificial intelligence, technology acceptance model, foreign language learning, behavioral intention, higher education, VietnamAbstract
During the ongoing digital transformation, a growing range of artificial intelligence (AI) applications has become available to support foreign-language learning. Grounded in the Technology Acceptance Model (TAM), this study examines how perceived usefulness (PU) and perceived ease of use (PEOU) shape university students' attitudes (ATT) toward AI applications and, in turn, their intention to use these applications for learning an additional foreign language (INT). Data were collected through a cross-sectional, convenience-sample survey of 300 undergraduate students at the University of Finance – Marketing (UFM), Vietnam, who reported prior experience using AI applications for language learning; responses were analyzed using PLS-SEM. The results show that both PEOU (β = 0.756, p < 0.001) and PU (β = 0.227, p = 0.004) positively predict ATT (adjusted R² = 0.659), and that ATT in turn positively predicts INT (β = 0.594, p < 0.001; adjusted R² = 0.350), with PEOU exerting a substantially stronger influence than PU. Because the design is cross-sectional and self-reported, and because the sample was restricted to students who had already adopted AI applications, the findings describe behavioral intention rather than actual use or learning effectiveness. The study extends TAM to the context of AI-assisted additional-language learning and offers universities practical guidance on promoting the usability of AI tools in language teaching.
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References
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
Alhamami, M. (2017). Beliefs about and intention to learn a foreign language in face-to-face and online settings. Computer Assisted Language Learning, 31(1–2), 90–113. https://doi.org/10.1080/09588221.2017.1387154
Bai, B., & Wang, J. (2020). The role of growth mindset, self-efficacy, and intrinsic value in self-regulated learning and English language learning achievements. Language Teaching Research, 27(1), 207–228. https://doi.org/10.1177/1362168820933190
Chou, C., Chen, K., & Hung, C. (2021). A study on flipped learning concerning learning motivation and learning attitude in language learning. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.753463
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Feng, L. (2025). Investigating the effects of artificial intelligence-assisted language learning strategies on cognitive load and learning outcomes: A comparative study. Journal of Educational Computing Research, 62(8), 1741–1774. https://doi.org/10.1177/07356331241268349
Gardner, R. C. (1985). Social psychology and second language learning: The role of attitudes and motivation. https://ci.nii.ac.jp/ncid/BA00118070.
Huang, F., Wang, L., Zou, B., & Wang, C. (2025). Understanding learner motivation and intention to use ChatGPT for English language learning. Discover Computing, 28(1), 293. https://doi.org/10.1007/s10791-025-09805-w
Ishida, A., Manalo, E., & Sekiyama, T. (2024). Students’ motivation to learn English: The importance of external influence on the ideal L2 self. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1264624
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the theory of planned behavior. Technology in Society, 77, Article 102521. https://doi.org/10.1016/j. techsoc.2024.102521
Kanont, K., Pingmuang, P., Simasathien, T., Wisnuwong, S., Wiwatsiripong, B., Poonpirome, K., … Khlaisang, J. (2024). Generative AI, a learning assistant? Factors influencing higher-ed students’ technology acceptance. The Electronic Journal of ELearning, 22(6), 18–33. https://doi.org/10.34190/ejel.22.6.3196
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lashari, A. A., Mashori, G. M., Abbasi, A. M., & Talpur, Q. (2017). Motivation to learn English language: A study of Shah Abdul Latif University, Khairpur, Sindh. International Journal of English Linguistics, 8(1), Article 15. https://doi.org/10.5539/ ijel.v8n1p15
Li, X., Wang, Z., & Xie, J. (2024). The mediating role of intention of learning behaviour in learning behaviour. Frontiers in Psychology, 15. https://doi.org/10.3389/ fpsyg.2024.1228783
Ma, D., Akram, H., & Chen, I.-H. (2024). Artificial intelligence in higher education: A cross-cultural examination of students’ behavioral intentions and attitudes. The International Review of Research in Open and Distance Learning, 25(3), 134–157. https://doi.org/10.19173/irrodl.v25i3.7703
Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021). Adoption of Chatbots for learning among university students: Role of perceived convenience and enhanced performance. International Journal of Emerging Technologies in Learning (iJET). https://doi.org/10.3991/ijet.v16i18.24315
Mendez, L. (2018). Exploring the relationship between learning strategies and English Language Proficiency. Journal of Language Teaching and Research, 9(5), 1017–1025.
Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666–125678. https://doi.org/10.1109/ access.2022.3225555
Songkram, N., Chootongchai, S., Osuwan, H., Chuppunnarat, Y., & Songkram, N. (2023). Students’ adoption towards behavioral intention of digital learning platform. Education and Information Technologies, 28(9), 11655–11677. https://doi.org/ 10.1007/s10639-023-11637-4
Sun, K., Chew, F. P., Lau, S. K., & Jing, Q. (2025). Instrumental and integrative motivations predict intention to use generative AI chatbots for English learning among undergraduates with non-English majors in Chinese private universities. Discover Computing, 28(1), 242. https://doi.org/10.1007/s10791-025-09753-5
Terblanche, N. H. D, & Kidd, M. (2022). Adoption factors and moderating effects of age and gender that influence the intention to use a non-directive reflective coaching chatbot. Sage Open, 12(2). https://doi.org/10.1177/21582440221096136
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, X., & Liu, H. (2024). Exploring the moderating roles of emotions, attitudes, environment, and teachers in the impact of motivation on learning behaviours in students’ English learning. Psychological Reports. https://doi.org/10.1177/ 00332941241231714
Wu, C., Liu, C., & Huang, Y. (2022). The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load. International Journal of STEM Education, 9(1). https://doi.org/10.1186/s40594-022-00346-y
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Copyright (c) 2026 Nguyen Pham Hanh Phuc, Phung Vu Bao Ngọc, Vu Van Anh (Author)
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