EventPulse: Connecting Student and Staff with Timely, AI-Driven Event Alert

Communication inefficiencies in higher education institutions often lead to low student engagement with campus events, hindering the development of a vibrant academic community. Existing communication channels—such as email, social media, and physical notice boards—are fragmented and fail to deliver personalized, timely information to a digitally native student body. This paper introduces EventPulse, a novel framework and mobile application designed to resolve these challenges through an AI-driven, context-aware notification system. EventPulse employs a hybrid filtering model, combining user roles, declared interests, and real-time contextual data to generate and deliver highly relevant event alerts. We detail the system’s architecture, the mathematical formulation of its personalization engine, and its implementation. A mixed-methods evaluation was conducted with a cohort of 120 students and faculty over a four-week period. The results demonstrate that EventPulse significantly outperforms traditional methods, increasing event participation by an average of 45% and achieving a 92% user satisfaction rating for notification relevance. This work validates the efficacy of personalized, intelligent systems in enhancing campus communication and provides a scalable, empirically-tested framework for fostering student engagement in modern academic environments.