An AI-Driven Predictive Model for Early Detection and Prevention of Student Dropouts

Abstract

Student dropouts represent a significant challenge in educational institutions, often leading to reduced academic success and resource inefficiency. To address this issue, this paper presents an AI-driven predictive model designed for the early detection and prevention of student dropouts. By leveraging machine learning algorithms and historical student data, the model identifies at-risk students based on academic performance, engagement metrics, attendance records, and socio-demographic factors. The predictive model uses classification techniques to flag potential dropouts and provides real-time insights to educators, allowing timely interventions such as personalized counseling and support programs. Preliminary results from pilot testing in several institutions demonstrate the model’s ability to achieve high accuracy in predicting student dropouts, leading to a reduction in dropout rates and improved retention.