International Journal of Scientific Research and Engineering Development

Alumni–student engagement has evolved from static communication into a dynamic, data-driven ecosystem. Traditional alumni management platforms primarily focus on event coordination and record maintenance, but lack personalization, intelligence, and adaptive interaction. Recent advancements in Artificial Intelligence (AI) enable the automation of mentor– mentee matching based on skills, interests, and professional attributes, thereby enhancing the relevance and quality of alumni–student interactions. This paper reviews existing alumni engagement systems, identifies key technological limitations, and emphasizes the growing need for AI-based recommendation mechanisms within academic ecosystems. While visualization dashboards continue to support transparency and performance tracking, AI-driven recommendations emerge as the core enabler of personalized engagement. The paper integrates insights from contemporary literature and proposes a conceptual direction for building intelligent, scalable, and institution-centric engagement platforms.

Muskan Verma, Rohit Mhetre, Onkar Gadge, R.A.Nikam,"A Review on Alumni–Student Engagement Systems and AI Recommenders in Higher Education" International Journal of Scientific Research and Engineering Development, V9(2): Page(2068-2076) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.