AI - Enhanced Academic Project Monitoring System

Abstract

The AI-Enhanced Academic Project Monitoring System is an intelligent and scalable platform designed to transform the traditional approach to monitoring and evaluating final-year academic projects in higher education institutions. Conventional project tracking relies heavily on manual supervision and subjective assessment, often resulting in inconsistent evaluation, lack of transparency, and uneven contribution among team members. These challenges create accountability issues and make it difficult for faculty members to identify underperforming students within a group. To address these limitations, the proposed system introduces an automated, data-driven solution that integrates modern web technologies with machine learning techniques. The platform continuously monitors student activity through GitHub API integration, capturing key metrics such as commit frequency, code additions, and contribution consistency. This data is processed using the Isolation Forest algorithm, a robust anomaly detection model, to identify irregular activity patterns and flag students or teams at risk of falling behind. By leveraging intelligent analytics, the system ensures objective and unbiased evaluation of individual contributions. In addition to automated tracking, the system provides a collaborative ecosystem featuring an intelligent Kanban board for task management and threaded document annotations for detailed and contextual mentor feedback on weekly submissions. A background scheduling mechanism ensures real-time accuracy by synchronizing GitHub data daily without manual intervention. Furthermore, the platform generates automated insight reports to support final grading, significantly reducing faculty workload while improving evaluation efficiency. The proposed solution ultimately creates a transparent, accountable, and high-performance environment for academic project lifecycle management.