Enhancing Four-Dimensional Student Engagement Through Teacher-Guided AI in Vocational Informatics Education

Abstract

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.