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@article{197355,
        author = {Prof. Renuka Gavli and Yash Rakhunde and Pavan Tamhane and Sourabh Nayak},
        title = {Alumni Connect: An Automated Platform for Alumni Engagement and Career Readiness},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7850-7859},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197355},
        abstract = {In the contemporary educational ecosystem, alumni networks represent a significantly underutilized asset for student career development and institutional growth. Traditional alumni management systems, reliant on manual processes and static databases, suffer from data inaccuracy, poor engagement, and a lack of personalized interaction. This paper proposes "Alumni Connect," an intelligent and automated platform designed to bridge this gap. The system leverages process automation for robust alumni data management and integrates AI-driven features, including a recommendation engine for mentorship matching and sentiment analysis for monitoring interaction quality. Built with a scalable microservices architecture using React.js and Flask, the platform facilitates real-time communication through discussion forums, a dedicated job portal, and direct mentorship requests. By automating the entire data lifecycle and fostering a dynamic, interactive community, Alumni Connect effectively enhances student-alumni engagement, reduces administrative overhead, and strengthens the professional network, thereby preparing students for a successful transition from academia to industry.},
        keywords = {Alumni Management, Process Automation, Microservices Architecture, AI Recommendations, Sentiment Analysis, Real-time Communication, Career Readiness.},
        month = {April},
        }