Impact of AI-driven personalized learning on student engagement and decision-making competencies compared to traditional management education approaches

This systematic review synthesizes evidence from seven peer-reviewed studies (YYY.Yo) on the impact of AI-driven personalized learning in management education, compared to traditional one-size-fits-all approaches. We screened for studies involving university-level business and management students, AI interventions like adaptive platforms, machine learning algorithms (e.g., neural networks, genetic optimization), and controls using lecture-based instruction. Outcomes focused on engagement (e.g., time on task, motivation scales) and decision-making competencies (e.g., simulation efficiency). Findings reveal consistent advantages for AI personalization: student engagement improved by ۱۰% via behavioral metrics (regression coefficient = ۱, p < •, • * ) and scale ratings (from "," to ۲,۰), with enhanced intrinsic motivation, satisfaction, and autonomy per Self-Determination Theory. Academic performance rose ۱۰-۴۰% across grades, test scores, task completion speed, and comprehension. Decision-making showed a ۱۰% efficiency gain in one study, with artificial neural networks at ۹,۵% predictive accuracy. Mechanisms include tailored feedback and adaptive pathways fostering competence and relatedness. Challenges encompass digital literacy, privacy biases, and scalability. Overall, AI personalization outperforms traditional methods, promoting deeper engagement and competencies, though limited generalizability warrants broader trials.

کلیدواژه ها:

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