AI-Driven Personalized Learning and Its Impact on Student Engagement and Decision-Making Competencies in Management Education

A systematic review of peer-reviewed empirical studies (Y.YY_Y. TO) in management and business education reveals that AI-driven personalized learning significantly outperforms traditional approaches. Student engagement consistently improves by Your.%, evidenced through behavioral metrics (e.g., time-on-platform, interaction rates), self-reported motivation, autonomy, competence, and relatedness (Self- Determination Theory), with effect sizes reaching regression coefficients of •‚ (p < *, **\) and scale increases from "," to ε,V. Decision-making competencies show gains of ۱۰_Y.%, with neural networks achieving up to % predictive accuracy in simulations and marked improvements in strategic thinking and problem-solving efficiency. Overall academic performance rises ۱۰_۵.% across grades, test scores, task completion speed, and knowledge retention. These benefits stem from adaptive platforms, deep learning, optimization algorithms, generative AI, and hybrid systems delivering real-time individualized feedback and dynamic pathways. No study reported neutral or negative effects compared to lecture-based or one-size-fits-all models.

Keywords:

AI-driven personalized learning , Management education , Student engagement , Decision-making competencies , Adaptive learning platforms