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

A systematic review of ۱۲ peer-reviewed empirical studies (۲۰۲۳–۲۰۲۵) in management and business education reveals that AI-driven personalized learning significantly outperforms traditional approaches. Student engagement consistently improves by ۲۵–۳۰%, 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 ۰.۶۲ (p < ۰.۰۰۱) and scale increases from ۳.۳ to ۴.۷. Decision-making competencies show gains of ۱۵–۲۰%, with neural networks achieving up to ۹۲.۳% predictive accuracy in simulations and marked improvements in strategic thinking and problem-solving efficiency. Overall academic performance rises ۱۵–۳۰% 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.

کلیدواژه ها:

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