Assessing the Acceptance of the Innovative Lighting System Trainer Using the Technology Acceptance Model: Evidence from Technical Education Students

The integration of educational technology plays a pivotal role in enhancing technical instruction and learner engagement. This study evaluated the behavioral intention to use and accept the Innovative Lighting System Trainer (ILST) at North Eastern Mindanao State University, Cantilan Campus, employing the Technology Acceptance Model (TAM) as the theoretical framework. The study aimed to determine the levels of perceived usefulness, perceived ease of use, attitude toward use, and behavioral control, and to examine the relationships among these constructs. A descriptive–correlational research design was utilized, involving student respondents from technical and engineering programs during the Academic Year 2024–2025. Weighted means and Pearson correlation coefficients were computed to assess the acceptability of the ILST and the interrelationships among TAM constructs. Findings revealed that all TAM constructs were rated as Very Highly Acceptable (grand mean = 4.37). Perceived usefulness obtained the highest mean rating, while perceived ease of use was rated slightly lower but remained Highly Acceptable. Correlation analysis showed strong positive and statistically significant relationships among perceived usefulness, attitude toward use, and behavioral control, confirming the validity of TAM in this context. The study concludes that user acceptance of the ILST is primarily influenced by its perceived usefulness and positive attitude formation. Integrating the ILST into technical training programs is strongly recommended to promote active, technology-enhanced learning and improve skill-based competency.

Educational technology, Innovative Lighting System Trainer (ILST), Perceived usefulness

1. Al-Barghothi, M., Al-Tahat, K., & Al-Mahasneh, M. (2023). Modeling metaverse adoption in higher education using an extended Technology Acceptance Model (TAM). Education and Information Technologies, 28(9), 11763–11789. https://doi.org/10.1007/s10639-023-11714-3 [Google Scholar] [Crossref]

2. Anwar, M. N., Khan, F., & Hafeez, M. (2023). Effect of simulation-based learning on engineering students’ skill acquisition and self-efficacy. International Journal of Engineering Education, 39(3), 472-486. [Google Scholar] [Crossref]

3. Antonietti, A., Colombo, B., & Lupi, C. (2022). Exploring technology acceptance among teachers: A meta-analytic review of TAM studies in education (2015–2022). Computers & Education, 189, 104587. https://doi.org/10.1016/j.compedu.2022.104587 [Google Scholar] [Crossref]

4. Birhanemeskel, E. A. (2025). Students’ technology adoption for blended learning: The moderating role of perceived enjoyment. Education and Information Technologies, 30(1), 255-271. https://doi.org/10.1007/s10639-024-12361-x [Google Scholar] [Crossref]

5. Cattaneo, A., & Rauseo, M. (2025). Technology acceptance and self-efficacy in vocational education: Integrating TAM with experiential learning theory. Vocational Education Research Review, 57(2), 142-165. [Google Scholar] [Crossref]

6. Chen, P.-H., Wu, Y.-C., & Hsu, C.-C. (2025). User interface design factors affecting ease of use and learning satisfaction in simulation-based training environments. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2025.1214703 [Google Scholar] [Crossref]

7. Edumadze, J., Kwarteng, M., & Osei-Bonsu, P. (2022). Assessing perceived usefulness of simulation tools in technical and vocational education and training (TVET). Journal of Technical Education and Training, 14(3), 33-46. [Google Scholar] [Crossref]

8. Exploring Hands-On Activities in Cambodia. (2024). Enhancing technical skills through interactive, competency-based instruction. ASEAN Journal of Technical Education, 9(1), 77-89. [Google Scholar] [Crossref]

9. Fatokun, J. (2025). A longitudinal study on digital technology acceptance in STEM education using an extended TAM framework. Journal of Educational Computing Research, 63(2), 321-340. [Google Scholar] [Crossref]

10. Giac, N. D., Nguyen, H. T., & Do, T. M. (2025). Institutional support and teacher attitudes toward adopting simulation technology in engineering education. Education Sciences, 15(2), 421. https://doi.org/10.3390/educsci15020421 [Google Scholar] [Crossref]

11. Hasan, A., Siddiqui, M. F., & Rao, N. (2023). Perceived usefulness vs. ease of use: Revisiting TAM determinants in utilitarian learning technologies. Computers in Human Behavior, 139, 107534. https://doi.org/10.1016/j.chb.2022.107534 [Google Scholar] [Crossref]

12. How do vocational teachers use technology? (2025). Exploring digital competence and technology adoption in TVET. International Journal of Vocational Education Research, 12(1), 15-29. [Google Scholar] [Crossref]

13. Juera, R. (2022). Digitalizing skills development using simulation-based mobile (SiM) learning application. International Journal of Technology and Engineering Studies, 8(3), 79-89. [Google Scholar] [Crossref]

14. Lin, T.-B., Chai, C.-S., & Hsu, C.-Y. (2023). Teachers’ technology acceptance in post-pandemic learning environments: A multi-level SEM analysis. Educational Technology Research and Development, 71(4), 2389-2412. [Google Scholar] [Crossref]

15. Msimango, B., Ncube, S., & Dlamini, M. (2024). Interactive technology in vocational training: Effects on learner motivation and practical performance. Journal of Vocational Education & Training, 76(3), 455-473. [Google Scholar] [Crossref]

16. Nguyen, P. T., & Tran, H. N. (2023). Experiential learning and technology acceptance among engineering students in Vietnam. International Journal of Engineering Pedagogy, 13(5), 56-72. [Google Scholar] [Crossref]

17. Pertiwi, S., Riyanto, B., & Lestari, I. (2023). Perceived usefulness and curriculum alignment of smart learning tools in engineering education. Education and Information Technologies, 28(6), 7891-7909. [Google Scholar] [Crossref]

18. Siliņa-Jasjukeviča, G., Kravale-Pauliņa, M., & Tihomirova, A. (2025). Students’ attitudes toward adopting simulation-based learning in technical education. Education and Information Technologies, 30(2), 1945-1961. https://doi.org/10.1007/s10639-024-12287-4 [Google Scholar] [Crossref]

19. Stöckl, A., & Struck, P. (2025). Integrating the Theory of Planned Behavior with TAM to examine vocational instructors’ digital tool adoption. Journal of Vocational Education Research, 59(1), 65-88. [Google Scholar] [Crossref]

20. Yao, Y., & Liu, J. (2025). Affective responses and behavioral intention in technology-mediated learning: Revisiting the TAM model. Computers & Education, 198, 104933. https://doi.org/10.1016/j.compedu.2025.104933 [Google Scholar] [Crossref]

21. Zhang, L., Wei, H., & Liu, X. (2025). Simulation-based learning for hands-on training in electrical systems: Evaluating perceived usefulness and ease of use. International Journal of Electrical Engineering Education, 62(2), 129-147. [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