چکیده مقاله
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 10% via behavioral metrics regression coefficient = 1, p < •, • and scale ratings from , to 2,0 , with enhanced intrinsic motivation, satisfaction, and autonomy per Self Determination Theory Academic performance rose 10 40% across grades, test scores, task completion speed, and comprehension Decision making showed a 10% efficiency gain in one study, with artificial neural networks at 9,5% 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
کلیدواژهها
نویسندگان
شیوه ارجاع
Rezvani, Bahram,1404,Impact of AI-driven personalized learning on student engagement and decision-making competencies compared to traditional management education approaches,The 11th international conference on key researches in management, accounting, banking and economics,Mashhad
ارائهشده در
مجموعه مقالات یازدهمین همایش بین المللی پژوهش های شاخص در مدیریت، حسابداری، بانکداری و اقتصاد28 آبان 1404 · مشهد