A Modular Web-Based Mentoring System with NLP Integration for Academic Support in Engineering Education

Effective academic mentoring is a critical component in the holistic development of undergraduate engineering students, contributing significantly to academic performance, career planning, and psychosocial well-being. This paper details the design and implementation of a scalable, modular mentoring application specifically developed to support structured and data-driven mentor-mentee interactions. The application architecture is based on a three-tier model encompassing a secure backend database, RESTful API services, and an intuitive front-end interface developed using modern web technologies. Core features include dynamic student profiling, automated meeting scheduling, feedback analytics, mentor dashboards, and performance monitoring via academic KPIs. Natural Language Processing (NLP) is integrated for sentiment analysis of mentee feedback and log entries, enabling proactive mentor interventions. The system’s flowchart, block diagram, and result visualization underscore its functional and architectural coherence. Usability testing across a pilot cohort demonstrated significant improvements in mentoring engagement, feedback quality, and data accessibility for institutional reporting. The proposed solution offers a robust framework for institutionalizing mentorship practices and aligns with NBA/NAAC accreditation parameters related to student support and progression.