Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article
Data sources: ZENODO
addClaim

Smart Campus Engagement System: An Integrated Web Platform With AI-Assisted Learning, Geolocation Attendance, And Real-Time Campus Services

Authors: B. Anief; M. Sakthivanitha;

Smart Campus Engagement System: An Integrated Web Platform With AI-Assisted Learning, Geolocation Attendance, And Real-Time Campus Services

Abstract

Contemporary higher education institutions operate a fragmented portfolio of digital tools — separate learning management systems, manual attendance registers, paper-based hostel outpass forms, and WhatsApp-group announcements — that impose coordination overhead on students and staff while producing no integrated data trail for institutional analytics. This paper presents the design, implementation, and evaluation of the Smart Campus Engagement System (SCES), a cloud-deployed, role-aware web platform that unifies nine functional modules — user management, AI-assisted learning, attendance and academics, hostel and outpass management, events and activities, communication and alerts, complaints and feedback, campus services, and analytics — within a single authenticated interface. The system is implemented using a Next.js 14 frontend, a FastAPI Python backend, a PostgreSQL cloud database, and a Groq API–powered LLaMA-3.3-70B language model for an AI assistant. Containerised deployment via Docker Compose supports horizontal scaling. System testing across eight functional scenarios at up to 200 concurrent users demonstrates API response times below 900 ms and a peak-load error rate of 2.8%. Security testing confirms resistance to SQL injection, JWT tampering, cross-site scripting, and unauthorised role escalation. Comparative analysis against four published smart campus systems confirms that the proposed implementation is the only system combining LLM-based AI assistance, geolocation-verified attendance, digital outpass workflow, and real-time push notifications in a single unified deployment. The system establishes a replicable, open-architecture blueprint for next-generation campus digitalisation.

Powered by OpenAIRE graph
Found an issue? Give us feedback