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The increasing rate of student dropouts is a significant challenge in education systems worldwide, affecting both academic progress and institutional sustainability. This research presents an AI-driven predictive model aimed at early detection and prevention of student dropouts. Leveraging advanced machine learning algorithms, including ensemble learning and deep learning techniques, the model analyzes a variety of student data such as academic performance, attendance, behavioral patterns, socio-economic factors, and psychological well-being. By identifying early warning signs of potential dropouts, the model provides actionable insights for educators and administrators to intervene promptly. Additionally, the system integrates personalized recommendations for targeted support, ensuring students receive the necessary resources to improve their academic engagement and performance. This predictive approach not only helps in reducing dropout rates but also contributes to fostering a more supportive learning environment. Experimental results demonstrate the effectiveness of the model, achieving high accuracy in dropout prediction and offering promising implications for its adoption in educational institutions
Kumar et al. (Fri,) studied this question.