The Influence of Instructional Leadership on the Use of Artificial Intelligence Applications among Vocational College Lecturers in Sarawak Zone

The integration of artificial intelligence (AI) in technical and vocational education and training (TVET) has gained increasing attention due to its potential to enhance instructional effectiveness. However, the level of AI application usage among vocational college lecturers remains uneven, particularly in less developed regions. This study examines the influence of instructional leadership on the use of AI applications among vocational college lecturers in the Sarawak Zone, guided by an integrated framework incorporating Instructional Leadership Theory, Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Efficacy Theory. A quantitative cross-sectional design was employed involving 120 lecturers. Data were collected using a structured questionnaire and analysed using descriptive and inferential statistics. The findings indicate that instructional leadership, technology acceptance, and self-efficacy significantly influence AI application usage, with technology acceptance emerging as the strongest predictor. The model explains a substantial proportion of variance in AI usage, highlighting the combined role of leadership and psychological factors. The study provides empirical support for an integrated perspective of AI adoption, emphasising that effective AI integration in vocational education is shaped not only by technological factors but also by leadership practices and lecturers’ readiness.

Instructional Leadership; Artificial Intelligence; Vocational Education

1. Ahmad, N., Ismail, R., & Hamzah, M. I. (2024). Digital transformation in Malaysian technical and vocational education: Policy directions and implementation challenges. Journal of Technical Education and Training, 16(1), 45–59. [Google Scholar] [Crossref]

2. Aidawati Abd Rahman. (2021). Kepimpinan Madani dan pembentukan nilai dalam pengurusan pendidikan. Jurnal Kepimpinan Pendidikan Malaysia, 8(2), 1–15. [Google Scholar] [Crossref]

3. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191 [Google Scholar] [Crossref]

4. Basir, M. A., Hassan, R., & Yusof, N. (2023). Teachers’ self-efficacy and readiness towards artificial intelligence integration in teaching and learning. International Journal of Instruction, 16(3), 321–338. [Google Scholar] [Crossref]

5. Che Mat, M. Z., & Abd Aziz, N. (2024). Artificial intelligence applications in technical and vocational education: Opportunities and pedagogical implications. Journal of TVET Studies, 6(1), 12–25. [Google Scholar] [Crossref]

6. Chen, L., Chen, P., & Lin, Z. (2023). Artificial intelligence in education: A review of current research and future prospects. Educational Technology & Society, 26(1), 1–15. [Google Scholar] [Crossref]

7. 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 [Google Scholar] [Crossref]

8. Hallinger, P., & Murphy, J. (1985). Assessing the instructional management behavior of principals. The Elementary School Journal, 86(2), 217–247. https://doi.org/10.1086/461445 [Google Scholar] [Crossref]

9. Hallinger, P., & Wang, W. C. (2020). Instructional leadership: A review of research and practice. Springer. https://doi.org/10.1007/978-3-030-39666-4 [Google Scholar] [Crossref]

10. Hassan, R., Basir, M. A., & Ismail, N. (2023). Organisational support and digital readiness in vocational institutions. Asian Journal of University Education, 19(2), 88–101. [Google Scholar] [Crossref]

11. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2021). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 2, 100019. https://doi.org/10.1016/j.caeai.2021.100019 [Google Scholar] [Crossref]

12. INJET. (2021). Artificial intelligence readiness in Malaysian TVET institutions. International Journal of Education and Training. [Google Scholar] [Crossref]

13. Kim, H., Lee, J., & Park, S. (2023). Professional development and technology integration: The mediating role of teacher self-efficacy. Teaching and Teacher Education, 121, 103908. [Google Scholar] [Crossref]

14. Leithwood, K., Harris, A., & Hopkins, D. (2021). Seven strong claims about successful school leadership revisited. School Leadership & Management, 41(1–2), 1–18. https://doi.org/10.1080/13632434.2020.1799777 [Google Scholar] [Crossref]

15. Mohd Faizal, N., & Norita, M. (2024). Intrinsic motivation and technology adoption among vocational educators. Malaysian Journal of Learning and Instruction, 21(1), 67–84. [Google Scholar] [Crossref]

16. Nguyen, T., Pham, H., & Tran, L. T. (2022). Educational leadership and technology adoption: Evidence from developing contexts. Educational Management Administration & Leadership, 50(4), 589–607. [Google Scholar] [Crossref]

17. Rahman, M. A., & Ismail, R. (2023). Digital literacy and AI adoption among vocational college lecturers in Malaysia. Journal of Technical Education and Training, 15(2), 101–115. [Google Scholar] [Crossref]

18. Rajapakse, D., Jayasena, S., & Fernando, P. (2024). Artificial intelligence adoption in education: A systematic review of teachers’ perspectives. Education and Information Technologies, 29(1), 345–372. [Google Scholar] [Crossref]

19. Ridzuan, M. A. (2025). Kepimpinan instruksional dan kesediaan teknologi dalam kalangan pensyarah TVET. Jurnal Pendidikan Malaysia, 50(1), 23–39. [Google Scholar] [Crossref]

20. Sun, A., Chen, X., & Ennis, C. D. (2021). Instructional leadership and technology integration in vocational education. Journal of Vocational Education & Training, 73(4), 567–585. [Google Scholar] [Crossref]

21. Sun, J., & Medaglia, R. (2024). Artificial intelligence in education: Challenges and implications for policy and leadership. Policy Futures in Education, 22(1), 3–21. [Google Scholar] [Crossref]

22. Tsai, Y. S., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2022). Learning analytics in higher education: A review of the literature. British Journal of Educational Technology, 53(1), 1–24. [Google Scholar] [Crossref]

23. 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. [Google Scholar] [Crossref]

24. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref]

25. Zhang, J., Yang, Y., & Li, H. (2021). Artificial intelligence and vocational education: A framework for skills development. Journal of Vocational Education & Training, 73(3), 435–452. [Google Scholar] [Crossref]

26. Zulkifli, R., & Razali, N. (2024). Kepimpinan pendidikan dan agenda pendigitalan institusi TVET di Malaysia. Jurnal Kepimpinan Pendidikan, 11(1), 55–70. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study