The use of Artificial Intelligence (AI) in education has brought about adaptive learning systems that can tailor instruction to fit the performance and engagement levels of students. While there has been some research into how AI is applied in general education, there has been less focus on its effects in Technical and Vocational Education and Training (TVET), where developing competencies and hands-on skills is key. This study looked into how an AI-driven adaptive learning system influenced technical skill mastery, self-regulated learning, and learner autonomy among the TVET students. We used a quasi-experimental pretest-posttest control group design with 180 second year technical college students. The experimental group (n=90) engaged with an AI-adaptive learning platform for eight weeks, while the control group (n=90) followed a traditional learning management system. We gathered data using a technical skill performance rubric, the Self-Regulated Learning Scale and the Learner Autonomy Questionnaire. The results from ANCOVA showed that students who used the AI-adaptive system achieved significantly higher skill mastery (F (1177) = 18.64, p <0.001), better self-regulated learning (F (1177) = 21.47, p<0,001|), and increased learner autonomy (F(1177) = 16.32, p<0.001) compared to those in the control group. Additionally, structural equation modeling indicated that self-regulated learning and learner autonomy played a partial mediating role in the connection between AI-adaptive learning and skill mastery. These findings suggest that AI-powered adaptive systems can significantly enhance competency-based technical training by promoting independent learning habits and improving practical performance outcomes.
Igwe et al. (Tue,) studied this question.