Enhancing Four-Dimensional Student Engagement Through Teacher-Guided AI in Vocational Informatics Education
DOI:
https://doi.org/10.29408/edumatic.v10i2.35138Keywords:
four-dimensional engagement, quasi-experimental study, student engagement, Teacher-guided AI, vocational Informatics educationAbstract
Although generative Artificial Intelligence (AI) is increasingly integrated into education, experimental evidence on how Teacher-guided AI influences multidimensional student engagement in vocational secondary education remains limited. This study examined whether Teacher-guided AI functions as a pedagogical scaffold that enhances student engagement in vocational Informatics education. A quasi-experimental pretest-posttest control group design was conducted with 49 Computer and Network Engineering students. Student engagement was assessed across behavioral, emotional, cognitive, and social dimensions using a validated questionnaire, supported by classroom observations and assignment documentation. The findings revealed a moderate positive effect on overall engagement (d = 0.58). Although the adjusted ANCOVA effect was marginal, the overall pattern favored the experimental group. Cognitive engagement emerged as the most responsive dimension, suggesting that Teacher-guided AI primarily supported knowledge construction and reflective inquiry, while emotional and social engagement demonstrated weaker effects. Observation data further indicated greater participation and deeper cognitive processing among students receiving Teacher-guided AI support. These findings suggest that AI is most effective when implemented as a pedagogical scaffold rather than an answer-generation tool. The study extends multidimensional engagement theory and provides empirical evidence for structured AI integration in vocational Informatics education.
References
Chen, X., Xie, H., Qin, S. J., Wang, F. L., & Hou, Y. (2025). Artificial Intelligence‐Supported Student Engagement Research: Text Mining and Systematic Analysis. European Journal of Education, 60(1), e70008. https://doi.org/10.1111/ejed.70008
Cohen, J. (2009). Statistical power analysis for the behavioral sciences (2. ed., reprint). Psychology Press.
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
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
Fütterer, T., Bardach, L., Kuhn, J., Keller, S. D., & Gerjets, P. (2026). Enhancing School Students’ Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial. Advance online publication. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10133-8
Hassan, A. (2026). Leveraging artificial intelligence (AI) to enhance student engagement and academic performance in higher education. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-026-13946-w
Heung, Y. M. E., & Chiu, T. K. F. (2025). How ChatGPT impacts student engagement from a systematic review and meta-analysis study. Computers and Education: Artificial Intelligence, 8, 100361. https://doi.org/10.1016/j.caeai.2025.100361
Hou, C., Zhu, G., & Sudarshan, V. (2025). The role of critical thinking on undergraduates’ reliance behaviours on generative AI in problem‐solving. British Journal of Educational Technology, 56(5), 1919–1941. https://doi.org/10.1111/bjet.13613
Järvelä, S., Nguyen, A., & Hadwin, A. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057–1076. https://doi.org/10.1111/bjet.13325
Jaya, H., Haryoko, S., Anwar, B., Lu’mu, & Kasau, S. (2024). Integration of Artificial Intelligence in Learning at Vocational Schools: Building Sustainable Collaboration for Educational Innovation in the Industrial Era 4.0. In A. Aswi, N. Djam’an, M. I. Pratama, & M. Ikram (Eds.), Proceedings of the International Conference on Sciences, Technology and Education (ICSTE 2024) (Vol. 898, pp. 127–139). Atlantis Press SARL. https://doi.org/10.2991/978-2-38476-335-1_12
Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., S, M., & Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in Psychology, 16, 1550621. https://doi.org/10.3389/fpsyg.2025.1550621
Kim, J., & Moon, J. (2025). Determinants of Usefulness of Chat GPT for Learning in Technology Acceptance Model (TAM) Using Information Credibility, Fun, and Responsiveness and Moderating Role of Fun. Sage Open, 15(1), 21582440251320173. https://doi.org/10.1177/21582440251320173
Lee, H.-Y., Chen, P.-H., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2024). Empowering ChatGPT with guidance mechanism in blended learning: Effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 21(1), 16. https://doi.org/10.1186/s41239-024-00447-4
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), e0332175. https://doi.org/10.1371/journal.pone.0332175
Lo, C. K., Hew, K.F., & Jong, M. S. Y. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers and Education, (219), 105100. https://doi.org/https://doi.org/10.1016/j.compedu.2024.105100
Magwa, L. (2026). Artificial intelligence as a scaffolding tool for self-directed learning in ODeL environments: An instrumental case study of Zimbabwe Open University. Frontiers in Education, 11, 1793940. https://doi.org/10.3389/feduc.2026.1793940
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454
Wu, D., & Zhang, J. (2025). Generative artificial intelligence in secondary education: Applications and effects on students’ innovation skills and digital literacy. PLOS One, 20(5), e0323349. https://doi.org/10.1371/journal.pone.0323349
Yan, J., Tian, H., Sun, X., & Song, L. (2025). Role of artificial intelligence in enhancing competency assessment and transforming curriculum in higher vocational education. Frontiers in Education, 10, 1551596. https://doi.org/10.3389/feduc.2025.1551596
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
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