Keyword: student engagement

ABSTRACT: This study explored the effects of AI-integrated courses on academic performance and career adaptation among TVET students, with student engagement and student competency as key mediating variables. Research exploring how AI-integrated courses simultaneously influence both academic and career outcomes in the TVET context is still limited, despite increased interest in AI adoption in education. Based on Social Cognitive Theory, Self-Determination Theory, and Career Construction Theory, this study proposes an integrated conceptual model to explain how AI integration may shape student learning and career readiness in the AI era. A quantitative research approach was used, and survey data from 765 TVET students were analyzed using IBM SPSS Statistics 26 for descriptive statistics and SmartPLS 4 for path and mediation analyses. The results showed that AI-integrated courses did not have a significant direct effect on academic performance or career adaptation but significantly enhanced student engagement and student competency. Student engagement emerged as the strongest predictor of both academic performance and career adaptation, while student competency significantly influenced academic performance only. Additionally, mediation analysis showed that student engagement fully mediated the relationships between AI-integrated courses and both outcomes, whereas student competency partially mediated only the relationship with academic performance. The model exhibited moderate explanatory power and acceptable predictive relevance across key outcomes. Overall, the findings suggest that the effectiveness of AI-integrated courses depends more on fostering active student engagement than on technology alone. This study contributes to the literature by providing an integrated framework for understanding AI adoption in TVET and offering practical implications for designing engaging and competency-driven learning environments to enhance student success in the AI era.