Adoption of Generative AI Tools Among Pre-Service TVET Instructors: A Quantitative Assessment Using Utaut Framework
DOI:
https://doi.org/10.53840/myjict10-2-230Keywords:
Generative AI, TVET instructors, UTAUT modelAbstract
This study explores the adoption of Generative Artificial Intelligence (GenAI) among pre-service Technical and Vocational Education and Training (TVET) instructors. GenAI tools are becoming increasingly relevant as instructional aids, though their adoption within the TVET pre-service pipeline remains understudied. Anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT), this study investigates the influence of four key constructs: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions on behavioral intention to adopt GenAI tools. A quantitative survey was conducted involving individuals currently enrolled in the national pre-service TVET instructor program. These participants represent the next generation of educators being trained to deliver skills-based instruction aligned with emerging digital and technological advancements across various TVET fields. Data collection was conducted using a structured questionnaire, and the responses were analyzed using descriptive statistics, Pearson correlation, and multiple regression analyses to uncover key patterns and relationships among variables. Results indicate that all four constructs are positively correlated with behavioral intention, with performance expectancy showing the strongest association. Regression analysis confirmed performance expectancy as the dominant predictor, followed by effort expectancy and facilitating conditions, together explaining 82% of the variance in behavioral intention, while social influence was not a significant predictor when controlling for the other factors. These results suggest that future instructors are more likely to embrace GenAI when they perceive the tools to be useful and when adequate support systems, such as access to technology and training, are in place. The study contributes to the growing body of research on AI integration in TVET training and highlights the need for strategic interventions that enhance institutional readiness and digital competency among future TVET educators. Practical implications are discussed for policymakers, curriculum developers, and training providers aiming to embed GenAI tools effectively within the Malaysian TVET ecosystem.
Downloads
References
Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT model in the adoption of e-learning technologies: An empirical study in France-based banks. Journal of Global Information Management, 28(4), 38–51. https://doi.org/10.4018/JGIM.2020100103
Aksoy, I. G., & Toprak, E. (2024). AI adoption in accounting education: A UTAUT-based analysis of mediating and moderating mechanisms. Journal of Pedagogical Research, 8(1), 381–408.
Baharin, A. T., Sahadun, N. A., Ramli, S., & Liyana Redzuan, N. A. (2024). Exploring the adoption of Generative Artificial Intelligence by TVET students: A UTAUT analysis of perceptions, benefits, and implementation challenges. Behaviour & Information Technology, 43(6), 941–957.
Batucan, G. B., Gonzales, G. G., Balbuena, M. G., Pasaol, K. R. B., Seno, D. N., & Gonzales, R. R. (2022). An extended UTAUT model to explain factors affecting online learning system amidst COVID-19 pandemic: The case of a developing economy. Frontiers in Artificial Intelligence, 5, 768831. https://doi.org/10.3389/frai.2022.768831
Belkina, M., Daniel, S., Nikolic, S., Haque, R., Lyden, S., Neal, P., ... & Hassan, G. M. (2025). Implementing generative AI (GenAI) in higher education: A systematic review of case studies. Computers and Education: Artificial Intelligence, 100407.
Beri̇gel, D. S., Şilbir, L., & Şilbir, G. M. (2025). Integrating artificial intelligence (AI) into technical and vocational education and training (TVET): A PRISMA-based systematic review. Calitatea Vieții, 36(1), 1–27.
Bitemirova, S., Zholdasbekova, S., Mussakulov, K., Anesova, A., & Zhanbirshiyev, S. (2023). Pre-service TVET teachers' digital competence: Evidence from survey data. TEM Journal, 12(2).
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 831–846. https://doi.org/10.1080/03075079.2023.2171901
Chen, P. D., Lambert, R., & Chiarelli, S. (2022). Effect of a science-based TPACK program for elementary preservice teachers according to their gender. Contemporary Issues in Technology and Teacher Education, 22(3).
Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications.
Ghazi, F., Chong, G., Juhdiy, F., Khalid, A., Mazlan, M., & Ali, W. N. H. W. (2025). Adoption and Usage of Generative Artificial Intelligence (GenAI) Tools Among Bandar Penawar MARA Professional College Students. Innovative Teaching and Learning Journal, 9(1), 247-257.
Han, M., Mustafa, H., & Kharuddin, S. (2025). AI adoption in accounting education: A UTAUT-based analysis of mediating and moderating mechanisms. Journal of Pedagogical Research, 9(2), 191–205.
Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
Lawrence, E. M. (2004). Technology integration and pre-service teacher training: The effectiveness of embedding technology use in three pre-methods courses (Doctoral dissertation). Nova Southeastern University. https://nsuworks.nova.edu/gscis_etd/659/
Lee, A. H. C. (2025). Use of Generative Artificial Intelligence among Selected Pre-University Business Students at a Private University in Malaysia. Asia-Pacific Journal of Futures in Education and Society, 4(1), 1-20.
Or, C., & Chapman, E. (2021). An extended Unified Theory of Acceptance and Use of Technology model for education contexts. Journal of Applied Learning and Teaching, 4(2), 98–109. https://doi.org/10.37074/jalt.2021.4.2.16
Ranuharja, F., Rizal, F., Langeveldt, D., Ejjami, R., Torres-Toukoumidis, A., & Jalinus, N. (2025). Relevance and impact of generative AI in vocational instructional material design: A systematic literature review. Salud, Ciencia y Tecnología, 5, 1336.
Richard, G. S., Joseph, A., Elikem, K., & Edem, B. K. (2023). Technology integration in technical and vocational education and training (TVET): The role of the art teacher. Journal of Technical Education and Training, 15(4), 73–88. https://doi.org/10.30880/jtet.2023.15.04.007
Sekaran, U., & Bougie, R. (2016). Research Methods for Business: A Skill-Building Approach (7th ed.). Wiley.
Schretzlmaier, P., Hecker, A., & Ammenwerth, E. (2022). Suitability of the Unified Theory of Acceptance and Use of Technology Two (UTAUT2) model for predicting mHealth acceptance using diabetes as an example: Qualitative methods triangulation study. JMIR Human Factors, 9(1), e34918. https://doi.org/10.2196/34918
Tahir, M. M. (2023). Students' behavioural intention towards adoption of online education: A study of the extended UTAUT model. Journal of Learning for Development, 10(3), 392–410.
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
Wedlock, B. C., & Trahan, M. P. (2019). Revisiting the Unified Theory of Acceptance and Use of Technology (UTAUT) model and scale: An empirical evolution of educational technology. Research Issues in Contemporary Education, 4(1), 6–20.
Zary, A., & Zary, N. (2025). Artificial intelligence in technical and vocational education and training: Empirical evidence, implementation challenges, and future directions [Preprint]. https://doi.org/10.20944/preprints202504.2173.v1
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Malaysian Journal of Information and Communication Technology (MyJICT)
This work is licensed under a Creative Commons Attribution 4.0 International License.
