CampusNet: An AI-Augmented, Proximity-Preserving Social Platform for Campus Engagement

This paper presented CampusNet, a campus-bounded social platform that makes an institution’s existing community fabric visible and accessible while preserving the proximity that gives that fabric its developmental value. The work contributes a design principle (proximity-first engagement), its operationalization into a three-entity, two-channel model with computed roles and an applied-AI discovery layer, and a working, deployed prototype that demonstrates the feasibility of the approach. The system is honest about its stage: it shows that the principle is buildable and coherent, not that it measurably improves outcomes. The principal extension is a controlled evaluation — a deployment with a real student cohort and pre/post measures of engagement, discovery success, and belonging — which would convert the present feasibility claim into an empirical one and is the natural dissertation continuation. Technically, the discovery layer is designed to evolve into a two-stage architecture in which a local embedding model performs first-stage vector retrieval and the hosted language model operates only over the retrieved shortlist, decoupling discovery cost from catalogue size at institutional scale. Further documented paths include an automated regression suite with continuous integration, server-side upvote de-duplication, object storage for media, consolidation of the community channel onto the real-time transport, rule-conditioned assisted moderation, and a multi-institution deployment in which each campus remains its own bounded community while the platform hosts many — growth that multiplies bounded communities without widening any single boundary. Through every such path, the platform’s central commitment is preserved: proximity, the principle the literature recommends and commercial platforms erase, remains the operating commitment of the system at any scale.

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