Bringing AI into Teaching: Understanding Vietnamese Teachers’ Perspectives and Pedagogical Challenges
Artificial Intelligence (AI) is reshaping education across the Asia-Pacific, yet its integration depends on teachers’ readiness and perspectives.
- Pub. date: August 15, 2025
- Online Pub. date: July 01, 2025
- Pages: 335-347
- 270 Downloads
- 5045 Views
- 0 Citations
- #AI in education
- # digital transformation
- # educational policy
- # pedagogical challenges
- # teacher perspectives
- # UTAUT.
Abstract:
Artificial Intelligence (AI) is reshaping education across the Asia-Pacific, yet its integration depends on teachers’ readiness and perspectives. This study explores AI adoption among Vietnamese teachers, a critical lens for the region’s digital education reforms, using the Unified Theory of Acceptance and Use of Technology (UTAUT). Through Structural Equation Modeling (SEM) and Latent Dirichlet Allocation (LDA), we analyzed responses from 246 teachers nationwide. Results show attitude strongly predicts adoption intention, with privacy and ethical concerns shaping acceptance, though fears of AI dependence hinder uptake. Uniform challenges across urban-rural and STEM-non-STEM contexts suggest systemic barriers in Vietnam’s education system. Teachers foresee AI as a pedagogical assistant but highlight insufficient training and privacy risks as key obstacles. These findings underscore the need for Asia-Pacific-relevant policies—AI literacy programs, ethical governance, and equitable access—to foster sustainable integration. This research informs regional educational policy by offering a Vietnam-centric model for balancing technological innovation with pedagogical integrity, addressing shared challenges in the Asia-Pacific’s digital transformation.
ai in education digital transformation educational policy pedagogical challenges teacher perspectives utaut
Keywords: AI in education, digital transformation, educational policy, pedagogical challenges, teacher perspectives, UTAUT.
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References
Abadie, A., Chowdhury, S., & Mangla, S. K. (2024). A shared journey: Experiential perspective and empirical evidence of virtual social robot ChatGPT’s priori acceptance. Technological Forecasting and Social Change, 201, Article 123202. https://doi.org/10.1016/j.techfore.2023.123202
Ballenas, E. B., & Lasco, M. T. (2024). Exploring mathematics teachers’ behavioral intentions to use artificial intelligence through structural equation modeling. 2024 International Conference on TVET Excellence and Development (ICTeD) (pp. 195-200). https://doi.org/10.1109/ICTeD62334.2024.10844599
Bergdahl, N., & Sjöberg, J. (2025). Attitudes, perceptions and AI self-efficacy in K-12 education. Computers and Education: Artificial Intelligence, 8, Article 100358. https://doi.org/10.1016/j.caeai.2024.100358
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2001). Latent Dirichlet Allocation. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (Vol. 14). MIT Press. http://bit.ly/4l1WVN5
Bouchet-Valat, M. (2020). SnowballC (version 0.7.0). [Computer software]. http://bit.ly/3GeIKW3
Delello, J. A., Sung, W., Mokhtari, K., Hebert, J., Bronson, A., & De Giuseppe, T. (2025). AI in the classroom: Insights from educators on usage, challenges, and mental health. Education Sciences, 15(2), Article 113. https://doi.org/10.3390/educsci15020113
Delgado-Rodríguez, S., Domínguez, S. C., & Garcia-Fandino, R. (2023). Design, development and validation of an educational methodology using immersive augmented reality for STEAM education. Journal of New Approaches in Educational Research, 12, 19-39. https://doi.org/10.7821/naer.2023.1.1250
Duong, N. T., Pham, T. D. T., & Pham, V. K. (2024). A Comparative Study on AI-Based Learning Behaviors: Evidence from Vietnam. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2024.2430433
Epskamp, S. (2015). semPlot: Unified visualizations of structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 22(3), 474–483. https://doi.org/10.1080/10705511.2014.937847
Feinerer, I., Hornik, K., & Meyer, D. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 1–54. https://doi.org/10.18637/jss.v025.i05
Government of Vietnam. (2021). Quyết định số 2222/QĐ-TTg: Phê duyệt chương trình chuyển đổi số giáo dục nghề nghiệp đến năm 2025, định hướng đến năm 2030 [Decision No. 2222/QĐ-TTg: Approving digital transformation program in vocational education until 2025 and orientation until 2030]. Prime Minister’s Office. https://chinhphu.vn/?pageid=27160&docid=204888&type=1&tagid=7
Haviz, M., Maris, I. M., Azis, D., & Helmita, R. (2024). An investigation into science motivation, technology acceptance, and satisfaction intention during the transition to E-learning of prospective biology teachers. Cogent Education, 11(1), Article 2393065. https://doi.org/10.1080/2331186X.2024.2393065
Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-Enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178, 977-1007. https://doi.org/10.1007/s10551-022-05049-6
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024). university teachers’ views on the adoption and integration of generative AI tools for student assessment in higher Education. Education Sciences, 14(10), Article 1090. https://doi.org/10.3390/educsci14101090
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36(20), 1902-1911. https://doi.org/10.1080/10447318.2020.1801227
Le, K. C., Tran, T. B. K., Nguyen, M. H., & Luu, H. D. (2024). Digital transformation in the education sector in Vietnam. In T. L. Nguyen, A. T. Nguyen, E. Ślęzak-Belowska, & M. Salamaga (Eds.), Economic and Political Aspects of EU-Asian Relations (pp. 359-366). Springer. https://doi.org/10.1007/978-981-99-8945-4_22
Li, Q., Liu, Q., & Chen, Y. (2023). Prospective teachers’ acceptance of virtual reality technology: A mixed study in rural China. Education and Information Technologies, 28, 3217-3248. https://doi.org/10.1007/s10639-022-11219-w
Ma, T. (2025). Systematically visualizing ChatGPT used in higher education: Publication trend, disciplinary domains, research themes, adoption and acceptance. Computers and Education: Artificial Intelligence, 8, Article 100336. https://doi.org/10.1016/j.caeai.2024.100336
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, 12167-12195. https://doi.org/10.1007/s10639-023-12333-z
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 262-272. https://aclanthology.org/D11-1024/
Mindrila, D. (2010). Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate non-normal data. International Journal of Digital Society, 1(1), 60-66. https://doi.org/10.20533/ijds.2040.2570.2010.0010
Nguyen, A. H. D., Le, T. T., Dang, T.-Q., & Nguyen, L.-T. (2024). Understanding metaverse adoption in education: The extended UTAUMT model. Heliyon, 10(19), Article e38741. https://doi.org/10.1016/j.heliyon.2024.e38741
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28, 4221-4241. https://doi.org/10.1007/s10639-022-11316-w
Niemi, H. (2024). AI in education and learning: Perspectives on the education ecosystem. In M. Streit-Bianchi, & V. Gorini (Eds.), New Frontiers in Science in the Era of AI (pp. 169-194). Springer. https://doi.org/10.1007/978-3-031-61187-2_11
Ogbo-Gebhardt, E., & Ogbo, O. (2024). Using a Large Language Model-Powered Assistant in Teaching: Stories of Acceptance, Use, and Impact among Ethnic Minority Students. 24th Biennial Conference of the International Telecommunications Society. Econstor. https://hdl.handle.net/10419/302517
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024). Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework. Heliyon, 10(18), Article e37569. https://doi.org/10.1016/j.heliyon.2024.e37569
Roberts, C. B. (2024). Examining high school teacher perspectives on the use of ChatGPT for teaching and learning [Doctoral dissertation, Youngstown State University]. OhioLINK. https://bit.ly/4lvXrD6
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. http://www.jstatsoft.org/v48/i02/
Sharma, S., & Singh, G. (2024). Adoption of artificial intelligence in higher education: An empirical study of the UTAUT model in Indian universities. International Journal of System Assurance Engineering and Management. Advance online publication. https://doi.org/10.1007/s13198-024-02558-7
Silge, J., & Robinson, D. (2016). tidytext: Text mining and analysis using tidy data principles in R. Journal of Open Source Software, 1(3), Article 37. https://doi.org/10.21105/joss.00037
Spante, M., Hashemi, S. S., Lundin, M., & Algers, A. (2018). Digital competence and digital literacy in higher education research: Systematic review of concept use. Cogent Education, 5(1), Article 1519143. https://doi.org/10.1080/2331186X.2018.1519143
Tram, N. H. M. (2024). Unveiling the drivers of AI integration among language teachers: Integrating UTAUT and AI-TPACK. Computers in the Schools, 42(2), 100-120. https://doi.org/10.1080/07380569.2024.2441155
Velli, K., & Zafiropoulos, K. (2024). Factors that affect the acceptance of educational AI tools by Greek teachers-A structural equation modelling study. European Journal of Investigation in Health, Psychology and Education, 14(9), 2560-2579. https://doi.org/10.3390/ejihpe14090169
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
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
Zaim, M., Arsyad, S., Waluyo, B., Ardi, H., Al Hafizh, M., Zakiyah, M., Syafitri, W., Nusi, A., & Hardiah, M. (2024). AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory. Computers and Education: Artificial Intelligence, 7, Article 100335. https://doi.org/10.1016/j.caeai.2024.100335
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), Article 49. https://doi.org/10.1186/s41239-023-00420-7