Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency
Interdisciplinary Educational Technology, 2(1), 2026, e109, https://doi.org/10.71176/interedtech/18931
Publication date: Jul 06, 2026
ABSTRACT
This study explored the effects of AI-integrated courses on academic performance and career adaptation among TVET students, with student engagement and student competency as key mediating variables. Research exploring how AI-integrated courses simultaneously influence both academic and career outcomes in the TVET context is still limited, despite increased interest in AI adoption in education. Based on Social Cognitive Theory, Self-Determination Theory, and Career Construction Theory, this study proposes an integrated conceptual model to explain how AI integration may shape student learning and career readiness in the AI era. A quantitative research approach was used, and survey data from 765 TVET students were analyzed using IBM SPSS Statistics 26 for descriptive statistics and SmartPLS 4 for path and mediation analyses. The results showed that AI-integrated courses did not have a significant direct effect on academic performance or career adaptation but significantly enhanced student engagement and student competency. Student engagement emerged as the strongest predictor of both academic performance and career adaptation, while student competency significantly influenced academic performance only. Additionally, mediation analysis showed that student engagement fully mediated the relationships between AI-integrated courses and both outcomes, whereas student competency partially mediated only the relationship with academic performance. The model exhibited moderate explanatory power and acceptable predictive relevance across key outcomes. Overall, the findings suggest that the effectiveness of AI-integrated courses depends more on fostering active student engagement than on technology alone. This study contributes to the literature by providing an integrated framework for understanding AI adoption in TVET and offering practical implications for designing engaging and competency-driven learning environments to enhance student success in the AI era.
KEYWORDS
AI-integrated courses in TVET student engagement student competency academic performance career adaptation
CITATION (APA)
Mean, V. (2026). Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency. Interdisciplinary Educational Technology, 2(1), e109. https://doi.org/10.71176/interedtech/18931
Harvard
Mean, V. (2026). Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency. Interdisciplinary Educational Technology, 2(1), e109. https://doi.org/10.71176/interedtech/18931
Vancouver
Mean V. Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency. Interdisciplinary Educational Technology. 2026;2(1):e109. https://doi.org/10.71176/interedtech/18931
AMA
Mean V. Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency. Interdisciplinary Educational Technology. 2026;2(1), e109. https://doi.org/10.71176/interedtech/18931
Chicago
Mean, Vandet. "Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency". Interdisciplinary Educational Technology 2026 2 no. 1 (2026): e109. https://doi.org/10.71176/interedtech/18931
MLA
Mean, Vandet "Preparing TVET Students for the AI Era: Effects of AI-Integrated Courses on Academic Performance and Career Adaptation through Student Engagement and Competency". Interdisciplinary Educational Technology, vol. 2, no. 1, 2026, e109. https://doi.org/10.71176/interedtech/18931
REFERENCES
- Ajani, O. A., Gamede, B., & Matiyenga, T. C. (2025). Leveraging artificial intelligence to enhance teaching and learning in higher education: Promoting quality education and critical engagement. Journal of Pedagogical Sociology and Psychology, 7(1), 54–69. https://doi.org/10.33902/JPSP.202528400
- Akkermans, J., Paradniké, K., der Heijden, B. I. J. M., & De Vos, A. (2018). The best of both worlds: The role of career adaptability and career competencies in students’ well-being and performance. Frontiers in Psychology, 9, Article 01678. https://doi.org/10.3389/fpsyg.2018.01678
- Alamsyah, M. N., Nuha, M. S., Muslihati, M., & Zamroni, Z. (2024). Learning engagement; Definition, aspects, measurement and intervention strategies. KONSELING: Jurnal Ilmiah Penelitian Dan Penerapannya, 6(1), 13–18. https://doi.org/10.31960/konseling.v6i1.2364
- Alexis, N. G., & Pavlatou, E. A. (2026). Exploring AI literacy: Voice recognition project in vocational education. Digital, 6(1), Article 19. https://doi.org/10.3390/digital6010019
- Asad, M. M., & Anwar, K. (2025). Influence of artificial intelligence on students’ career competencies and career resources: A global perspective. The International Journal of Information and Learning Technology, 42(4), 366–391. https://doi.org/10.1108/IJILT-05-2024-0091
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc. https://books.google.com.kh/books?id=k6a3AAAAIAAJ&redir_esc=y
- Brew, E. A., Nketiah, B., Koranteng, R., Brew, E. A., Nketiah, B., & Koranteng, R. (2021). A literature review of academic performance, an insight into factors and their influences on academic outcomes of students at senior high schools. Open Access Library Journal, 8, Article e7423. https://doi.org/10.4236/oalib.1107423
- Çali, M., Lazimi, L., & Ippoliti, B. M. L. (2024). Relationship between student engagement and academic performance. International Journal of Evaluation and Research in Education, 13(4), 2211–2218. https://doi.org/10.11591/ijere.v13i4.28710
- Chiu, T. K. F., Çoban, M., Sanusi, I. T., & Ayanwale, M. A. (2025). Validating student AI competency self-efficacy (SAICS) scale and its framework. Educational Technology Research and Development, 73(4), 2785–2807. https://doi.org/10.1007/s11423-025-10512-y
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Ejjami, R. (2024). AI’S impact on vocational training and employability: Innovation, challenges, and perspectives. International Journal for Multidisciplinary Research, 6(4), 1-32. https://doi.org/10.36948/ijfmr.2024.v06i04.24967
- Feng, S., & Carolus, A. (2026). Artificial intelligence literacy at school: A systematic review with a focus on psychological foundations. Computers and Education: Artificial Intelligence, 10, Article 100551. https://doi.org/10.1016/j.caeai.2026.100551
- Fortuna, A., Prasetya, F., Samala, A. D., Rawas, S., Criollo-C, S., Kaya, D., Raihan, M., Andriani, W., Safitri, D., & Nabawi, R. A. (2025). Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities. Social Sciences & Humanities Open, 12(3), Article 102114. https://doi.org/10.1016/j.ssaho.2025.102114
- Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
- Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of reflective measurement models. In J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, & S. Ray (Eds.), Partial least squares structural equation modeling (PLS-SEM) using R (pp. 75–90). Springer. https://doi.org/10.1007/978-3-030-80519-7_4
- Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. Journal of Educational Research, 98(3), 184–192. https://doi.org/10.3200/JOER.98.3.184-192
- Jerez, S. A. R., Casas, J. S. L., & Osorio, M. R. (2025). Integration of generative artificial intelligence and 3D immersive environments in competency-based higher education. Educational Process: International Journal, 19, Article e2025544. https://doi.org/10.22521/edupij.2025.19.544
- Jiang, Z., Chen, B., & Gao, R. (2024). Exploring the relationship between student engagement and role of career adaptability to enhance employability of university graduates. International Journal of Management Thinking, 2(2), 20–44. https://doi.org/10.56868/ijmt.v2i2.58
- Kenayathulla, H. B., Ahmad, N. A., & Idris, A. R. (2019). Gaps between competence and importance of employability skills: Evidence from Malaysia. Higher Education Evaluation and Development, 13(2), 97–112. https://doi.org/10.1108/heed-08-2019-0039
- Khairuddin, Z., Shahabani, N. S., Ahmad, S. N., Ahmad, A. R., & Zamri, N. A. (2024). Students’ perceptions on the artificial intelligence (AI) tools as academic support. Malaysian Journal of Social Sciences and Humanities, 9(11), Articlr e003087. https://doi.org/10.47405/mjssh.v9i11.3087
- Kimutai, S. K., Kitonyi, T., & Kimitei, E. K. (2025). An evaluation of Kenya TVET trainers on use of AI in instruction design and delivery. Africa Journal of Technical and Vocational Education and Training, 10(1), 13–27. https://doi.org/10.69641/afritvet.2025.101193
- Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://cits.tamiu.edu/kock/pubs/journals/2015JournalIJeC_CommMethBias/Kock_2015_IJeC_CommonMethodBiasPLS.pdf
- Kovari, A. (2025). A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade. Social Sciences & Humanities Open, 11(1), Article 101335. https://doi.org/10.1016/j.ssaho.2025.101335
- Leong, W. Y. (2025). Artificial intelligence, automation, and technical and vocational education and training: Transforming vocational training in digital era. Engineering Proceedings, 103(1), Article 9. https://doi.org/10.3390/engproc2025103009
- Lin, H., & Chen, Q. (2024). Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: Students and teachers’ perceptions and attitudes. BMC Psychology, 12(1), Article 487. https://doi.org/10.1186/s40359-024-01979-0
- Lin, X., Xu, G., & Xiong, B. (2025). Artificial intelligence literacy, sustainability of digital learning and practice achievement: A study of vocational college students. PLOS One, 20(10), Article e0332175. https://doi.org/10.1371/journal.pone.0332175
- Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, 1-16. https://doi.org/10.1145/3313831.3376727
- Merino-Campos, C. (2025). The impact of artificial intelligence on personalized learning in higher education: A systematic review. Trends in Higher Education, 4(2), Article 17. https://doi.org/10.3390/higheredu4020017
- Oliveira, Í. M., & Marques, C. (2024). The role of career adaptability and academic engagement in college student’s life satisfaction. International Journal of Environmental Research and Public Health, 21(5), Article 596. https://doi.org/10.3390/ijerph21050596
- Poláková, M., Suleimanová, J. H., Madzík, P., Copuš, L., Molnárová, I., & Polednová, J. (2023). Soft skills and their importance in the labour market under the conditions of Industry 5.0. Heliyon, 9(8), Article e18670. https://doi.org/10.1016/j.heliyon.2023.e18670
- Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
- Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
- Sahito, Z. H., Khoso, F. J., & Phulpoto, J. (2025). The effectiveness of active learning strategies in enhancing student engagement and academic performance. Journal of Social Sciences Review, 5(1), 110–127. https://doi.org/10.62843/jssr.v5i1.471
- Sajja, R., Sermet, Y., Fodale, B., & Demir, I. (2026). Evaluating AI-powered learning assistants in engineering higher education with implications for student engagement, ethics, and policy. Scientific Reports, 16(1), Article 7565. https://doi.org/10.1038/s41598-026-39237-5
- Savickas, M. L. (2005). The theory and practice of career construction. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 42–70). Hoboken, NJ: John Wiley. https://onlinelibrary.wiley.com/doi/book/10.1002/9781394258994
- Savickas, M. L. (2013). Career construction theory and practice. In S. D. Brown & R. W. Lent (Ed.), Career development and counseling: Putting theory and research to work (2nd ed., pp. 147-183). John Wiley & Sons. https://library.strathmore.edu/GroupedWork/d2c94b68-0a08-0f19-a92c-901faf86d86b-eng/Home
- Savickas, M. L., & Porfeli, E. J. (2012). Career adapt-abilities scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of Vocational Behavior, 80(3), 661–673. https://doi.org/10.1016/j.jvb.2012.01.011
- Singh, E., Vasishta, P., & Singla, A. (2025). AI-enhanced education: Exploring the impact of AI literacy on generation Z’s academic performance in Northern India. Quality Assurance in Education, 33(2), 185–202. https://doi.org/10.1108/QAE-02-2024-0037
- Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4(1), Article 100127. https://doi.org/10.1016/j.caeai.2023.100127
- Steinmayr, R., Meißner, A., Weidinger, A. F., & Wirthwein, L. (2014). Academic achievement. In S. Faircloth (ed.), Oxford bibliographies in education. Oxford University Press. https://doi.org/10.1093/obo/9780199756810-0108
- Suleiman, I. B., Okunade, O. A., Dada, E. G., & Ezeanya, U. C. (2024). Key factors influencing students’ academic performance. Journal of Electrical Systems and Information Technology, 11(1), Article 41. https://doi.org/10.1186/s43067-024-00166-w
- Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), Article 343. https://doi.org/10.3390/educsci15030343
- Wang, L., & Chen, C. J. (2024). Factors affecting student academic performance: A systematic review. International Journal on Studies in Education, 7(1), 1–47. https://doi.org/10.46328/ijonse.276
- Wong, S.-C. (2020). Competency definitions, development and assessment: A brief review. International Journal of Academic Research in Progressive Education and Development, 9(3), 95-114. https://doi.org/10.6007/ijarped/v9-i3/8223
- Yaseen, H., Mohammad, A. S., Ashal, N., Abusaimeh, H., Ali, A., & Sharabati, A. A. A. (2025). The impact of adaptive learning technologies, personalized feedback, and interactive AI tools on student engagement: The moderating role of digital literacy. Sustainability, 17(3), Article 1133. https://doi.org/10.3390/su17031133
- Younas, M., El-Dakhs, D. A. S., & Noor, U. (2025). The impact of artificial intelligence-based learning tools in academic innovation: A review of Deep seek, GPT, and Gemini (2020–2025). Frontiers in Education, 10, Article 1689205. https://doi.org/10.3389/feduc.2025.1689205
- Zakir, S., Hoque, M. E., Susanto, P., Nisaa, V., Alam, M. K., Khatimah, H., & Mulyani, E. (2025). Digital literacy and academic performance: The mediating roles of digital informal learning, self-efficacy, and students’ digital competence. Frontiers in Education, 10, Article 1590274. https://doi.org/10.3389/feduc.2025.1590274
- Zary, A., & Zary, N. (2025). Artificial intelligence in technical and vocational education and training: Empirical evidence, implementation challenges, and future directions. Preprints. https://doi.org/10.20944/preprints202504.2173.v1
- Zhou, M., & Peng, S. (2025). The usage of AI in teaching and students’ creativity: The mediating role of learning engagement and the moderating role of AI literacy. Behavioral Sciences, 15(5), Article 587. https://doi.org/10.3390/bs15050587
- Zixuan, B., Omar, M. K., & Puad, M. H. M. (2025). Employability skills and career adaptability among TVET students: What matters? Asian Journal of Vocational Education and Humanities, 6(1), 1–13. https://doi.org/10.53797/ajvah.v6i1.1.2025
LICENSE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.