ScholarShield AI – Safeguarding Student’s Academic Futures

This project aims to address the growing challenge of student dropouts in educational institutions through the use of artificial intelligence, data analytics, and predictive modeling. It tackles key issues such as the lack of early identification mechanisms, manual monitoring of student performance, and limited data-driven intervention strategies. By collecting and analyzing student-related data including attendance, academic performance, behavioral indicators, and socio-economic factors, the system predicts dropout risk at an early stage and categorizes students into risk levels. The proposed system provides actionable insights and intervention recommendations to teachers and academic administrators, enabling timely support for at-risk students. ScholarSheild AI features an intuitive web-based dashboard that visualizes risk trends, individual student profiles, and institutional analytics. Built using a modern technology stack comprising React, Flask, and machine learning models, the system integrates real-world educational data to support informed decision-making. This paper presents the project’s background, objectives, methodology, system architecture, and its strong potential to improve student retention, academic outcomes, and institutional effectiveness in contemporary educational environments.