Higher education institutions worldwide operate under a persistent structural burden: academic data, administrative workflows, and student support mechanisms are distributed across disconnected, incompatible platforms, creating information asymmetry and disengaging students from institutional life. This paper presents the Smart Campus Engagement System (SCES), a full-stack, AI-augmented digital platform that addresses this fragmentation gap through a unified, role-differentiated interface connecting students, teaching staff, and administrators. The system is grounded in a three-tier microservices-inspired architecture comprising a Next.js frontend, a Python FastAPI backend, and a PostgreSQL database deployed via Docker containerisation. A five-role RBAC model (Admin, Staff, Student, Hosteller, Day Scholar) governs access, while a LLaMA-3.3-70B language model, served via the Groq cloud inference API, provides 24/7 conversational campus support. Seven interdependent functional modules cover user management, academic tracking, real-time WebSocket-based push notifications, hostel and outpass management, complaint escalation, event lifecycle management, and student engagement analytics. Iterative design-build-test methodology was applied across four development sprints. System evaluation encompassed functional testing (8 representative test cases, 100% pass rate), performance testing (312 ms mean login latency; 94 ms WebSocket broadcast latency to 200 concurrent clients), security assessment against the OWASP Top-10 taxonomy, and a formative usability study (n = 40, overall satisfaction mean 4.5/5.0). Results demonstrate that SCES successfully resolves the identified fragmentation gap: latency benchmarks are within interactive-system thresholds, role enforcement eliminates cross-role access violations, and user satisfaction significantly exceeds the pre-deployment baseline. The platform operationalises the Smart Campus 4.0 vision and provides a replicable blueprint for institutions seeking to consolidate academic and administrative services under a single AI-enabled environment.
Anief et al. (Thu,) studied this question.