Explainable AI-Based Early Dropout Risk Detection and Intervention System for Rural Schools

Abstract: Student dropout is a significant challenge in rural education, often associated with irregular attendance, poor academic performance, financial difficulties, and limited access to educational resources. This research proposes an Explainable AI-Based Early Dropout Risk Detection and Intervention System for Rural Schools to identify students who may be at risk of leaving school before completing their education. The proposed system analyses relevant student data, including attendance records, academic performance, and socio-economic indicators, to estimate dropout risk using machine learning techniques. Explainable Artificial Intelligence (XAI) is incorporated to provide understandable reasons behind each prediction, enabling teachers and school administrators to make informed decisions. Based on the identified risk factors, the system can support timely interventions such as counselling, remedial education, parental engagement, and referrals to available support schemes. The proposed approach aims to improve early identification, promote transparent decision-making, and support student retention in rural schools while maintaining data privacy and fairness. Overall, the system provides a data-driven framework for proactive educational support and more inclusive learning opportunities.