Academic Care: An AI-Powered Early Warning and Intervention System for Student Academic Support

Abstract

Academic institutions generate substantial academic information, but this information is often distributed across different records, stakeholders, and workflows, making it difficult to identify emerging academic difficulties and convert them into timely support. This paper presents Academic Care, a working prototype for academic early warning and student intervention. The system combines a transparent rule-based risk engine with AI-assisted explanation and personalized study planning. A role-based architecture provides separate experiences for students, teachers, and parents, while CSV-based data intake supports prototype data integration. The implemented system includes academic-risk classification and scoring, weaksubject or weak-topic identification, an AI study assistant, personalized study plans, teacher intervention, parent notifications or nudges, JWT authentication, charts and analytics, and feedback/progress tracking. MongoDB is used as the core database technology for the application. The prototype uses synthetic/generated student data and is intended as an engineering and workflow prototype rather than a validated institutional prediction model. Attendance tracking, assignment tracking, and marks/test tracking are not currently available as standalone implemented modules. Therefore, the study does not report fabricated predictive-performance statistics. The contribution of the work is an integrated architecture that connects academic risk detection with explanation, personalized guidance, and stakeholder intervention within a single support loop