Evaluating the Pedagogical Impact of AI-Powered Learning Platforms: An Empirical Study on Student Engagement and Academic Performance

This empirical study examines the predictive impact of artificial intelligence (AI)-driven pedagogical variables on students’ academic performance within the context of smart business education. Employing a cross-sectional quantitative design, data were collected from a purposive sample of 216 undergraduate and postgraduate business students using a structured, self-administered questionnaire as the primary research tool. The purpose of this study was to investigate the impact of various constructs, including AI usage frequency, quality of personalized learning, AI-enabled mentorship, and instructor digital competence, on academic outcomes in AI-integrated learning environments. Multiple regression analysis revealed that instructor digital competence was the most statistically significant predictor, followed by a moderate positive effect of personalized learning recommendations; conversely, AI usage frequency exhibited a slight but significant negative association with performance, while AI-enabled mentorship showed a non-significant positive trend. The model met all key statistical assumptions, including normality, linearity, and absence of multicollinearity. These findings underscore the pivotal role of human pedagogical agency and institutional support in enhancing the effectiveness of AI tools in education. The study has significant implications for academic policymakers, curriculum designers, and educators, advocating for the intentional integration of AI, faculty training, and infrastructure readiness to foster meaningful, equitable, and sustainable educational development in the era of algorithmic learning.