Instructors' and Students' Perceptions of AI-Supported Teaching and Learning in Technical-Vocational Education: A Systematic Literature Review

The integration of Artificial Intelligence (AI) within Technical-Vocational Education and Training (TVET) is increasingly shaping the educational landscape, presenting both novel pedagogical opportunities and complex challenges. This systematic literature review investigates how instructors and students perceive and experience the implementation of AI-supported teaching and learning approaches in TVET settings. This review addresses the growing need to understand the human dimensions of AI adoption in vocational education, particularly from the perspectives of those directly involved in teaching and learning. As AI technologies become increasingly embedded in educational practices, examining the experiences, perceptions, and concerns of instructors and students is essential for informing effective and responsible implementation strategies. By synthesizing evidence across diverse TVET contexts, this review provides insights into emerging opportunities, challenges, and implications for policy and practice. Following the PRISMA 2020 framework, an initial database search on Lens.org yielded 580 records. After applying rigorous inclusion and exclusion criteria, including filters for publication date, document type, and subject matter, 19 empirical studies were selected for final qualitative synthesis. The findings reveal three overarching themes. First, while instructors exhibit optimism regarding efficiency gains, they express significant concerns about dehumanization, cognitive decline, and threats to academic integrity. Second, students experience enhanced psychological safety through instant feedback, yet face a paradox of being "Engaged but Amotivated" alongside risks of severe technological dependency. Third, AI implementation in TVET is uniquely constrained by the necessity for tactile skill mastery, industry precision, and alignment with local cultural practices. Ultimately, maximizing AI's potential in vocational education requires addressing systemic infrastructure barriers and preserving the hands-on, human-centric core of TVET.

Vocational education and training, Artificial intelligence, Teaching and learning, Perception, Systematic review

1. Apoko, T. W. (2025). Generative artificial intelligence in English instruction: Indonesian EFL vocational high school teachers’ perspectives. JOLLT Journal of Languages and Language Teaching, 13(3), 1432–1444. https://doi.org/10.33394/jollt.v13i3.14190 [Google Scholar] [Crossref]

2. Cao, L., & Abdullah, A. (2025). EBA (Engaged but amotivated) in AI-enhanced EFL learning: A qualitative study from a Chinese higher vocational context. Frontiers in Psychology, 16, Article 1643653. https://doi.org/10.3389/fpsyg.2025.1643653 [Google Scholar] [Crossref]

3. Dahnial, Ninditama, I. P., Rahmawati, D., & Purwanto, M. B. (2025). Harnessing artificial intelligence (AI) in personalizing learning at Palembang vocational schools: An exploratory study. Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer, 5(3), 319–341. https://doi.org/10.55606/juisik.v5i3.1664 [Google Scholar] [Crossref]

4. Deng, X., Xu, Y., Hussin, S., & Sulaiman, N. A. (2025). Use of AI tools in EFL writing instruction: A case study of Chinese vocational college instructors. Forum for Linguistic Studies, 7(5), 114–129. https://doi.org/10.30564/fls.v7i5.9285 [Google Scholar] [Crossref]

5. Fitriantinah. (2026). Digital transformation: A qualitative study on the impact of artificial intelligence (AI)-based learning media on accounting learning motivation of Gen Z students in vocational high schools. Journal of Indonesian Social Sciences, 7(2), 622–628., [Google Scholar] [Crossref]

6. Ihsanurrahmah, F., Unsiah, F., Dewi, D. N., & Aiman, A. (2025). Pre-service teachers’ experiences with ChatGPT during a vocational high school teaching practicum. JOLLT Journal of Languages and Language Teaching, 13(3), 1481–1492. https://doi.org/10.33394/jollt.v13i3.14284 [Google Scholar] [Crossref]

7. Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M.-C., & Vedel, I. (2018). Mixed Methods Appraisal Tool (MMAT), version 2018. User guide. McGill University. http://mixedmethodsappraisaltoolpublic.pbworks.com/w/file/fetch/127916259/MMAT_2018_criteria-manual_2018-08-01_ENG.pdf [Google Scholar] [Crossref]

8. Kankeviciene, L., Gedviliene, G., & Supranaviciene, U. (2026). Integrating AI tools into learning: A study of students’ attitudes in universities, colleges, and vocational institutions. European Journal of Open, Distance and E-Learning, 28(1), 1–17. https://doi.org/10.65043/eurodl.165 [Google Scholar] [Crossref]

9. Lin, W. Q. (2026). Enhancing personalised learning with artificial intelligence - Implementation challenges from educators’ perspective in vocational education and training. Research and Practice in Technology Enhanced Learning, 21, Article 33. https://doi.org/10.58459/rptel.2026.21033 [Google Scholar] [Crossref]

10. Majid, N. W. A., Barri, M. A. F., Sari, A. P., Sodikin, R. A., Azman, M. N. A., & Prestoza, M. J. R. (2026). Vocational teachers’ insights on artificial intelligence and computational thinking integration. Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika, 10(1), 1–15. https://doi.org/10.36312/e-saintika.v10i1.3660 [Google Scholar] [Crossref]

11. Navasca, R. I. (2025). Bridging digital and vocational learning: The role of AI in enhancing culturally responsive TLE instruction and pedagogical approaches. International Journal on Culture, History, and Religion, 7(SI3), 479–494. https://doi.org/10.63931/ijchr.v7iSI3.398 [Google Scholar] [Crossref]

12. OECD. (2023). AI and the future of skills, volume 2: Methods for evaluating AI capabilities. OECD Publishing. https://doi.org/10.1787/a9fe53cb-en [Google Scholar] [Crossref]

13. OECD. (2023). Spotlight on vocational education and training: Findings from Education at a Glance 2023. OECD Publishing. https://doi.org/10.1787/acff263d-en [Google Scholar] [Crossref]

14. Petridou, E., & Lao, L. (2024). Identifying challenges and best practices for implementing AI additional qualifications in vocational and continuing education: A mixed methods analysis. International Journal of Lifelong Education, 43(4), 385–400. https://doi.org/10.1080/02601370.2024.2351076 [Google Scholar] [Crossref]

15. Pondelíková, I. (2026). The examination of contemporary conditions for the effective integration of AI in educational process as perceived by VET teachers in Slovakia. Scientific Societal & Behavioral Research Journal, 2(1), 14–32. https://doi.org/10.63329/av3nz12322 [Google Scholar] [Crossref]

16. Pondelíková, I., & Welnitzová, K. (2026). From needs analysis to training: Building AI literacy among VET teachers in Slovakia. R&E-SOURCE, 13(Special Issue 1). https://doi.org/10.53349/re-source.2026.is1.a1541 [Google Scholar] [Crossref]

17. Radita, C., & Yuniar, R. F. (2025). AI in vocational English classrooms: Mapping teachers’ knowledge, benefits, and pedagogical uses. Proceedings of International Conference on Islamic Education (ICIED), 10, 972–979. [Google Scholar] [Crossref]

18. Raharjo, M. A., Rossydi, A., Fachrurrazy, Octavianie, A., Idyaningsih, N., & Sukarman. (2025). Integrating artificial intelligence in vocational higher education: A case study at Politeknik Penerbangan Makassar. IJORER: International Journal of Recent Educational Research, 6(6), 1981–1997. https://doi.org/10.46245/ijorer.v6i6.1107 [Google Scholar] [Crossref]

19. Rott, K. J., Lao, L., Petridou, E., & Schmidt-Hertha, B. (2022). Needs and requirements for an additional AI qualification during dual vocational training: Results from studies of apprentices and teachers. Computers and Education: Artificial Intelligence, 3, Article 100102. https://doi.org/10.1016/j.caeai.2022.100102 [Google Scholar] [Crossref]

20. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research [Google Scholar] [Crossref]

21. Wang, F. (2024). AI in vocational training: A qualitative interview study of early-stage practitioners in the real estate brokerage industry in China. Voprosy obrazovaniya / Educational Studies Moscow, 2024(3), 180–200. https://doi.org/10.17323/vo-2024-17710 [Google Scholar] [Crossref]

22. Yang, L., & Balazon, F. G. (2026). The use of artificial intelligence in higher vocational colleges in Sichuan, China: A mixed-methods study of adoption, utilization, and policy implications. Frontiers in Education, 11, Article 1796803. https://doi.org/10.3389/feduc.2026.1796803 [Google Scholar] [Crossref]

23. Zahroh, A. A., Hikmat, M. H., & Wijayanto, A. (2026). Comparing AI and teacher feedback: Student insights on creativity and critical thinking. DIJEMSS: Dinasti International Journal of Education Management and Social Science, 7(2). https://doi.org/10.38035/dijemss.v7i2 [Google Scholar] [Crossref]

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