Continuous Engagement Detection in Smart Classrooms via Multicamera AI Systems in 5G-Enabled Educational Networks

This paper presents an engagement detection system for smart classrooms leveraging a private 5G network and Multiaccess Edge Computing (MEC). The architecture integrates IoT and computer vision to monitor student behavior through four strategically positioned cameras. By offloading video streams to the edge, the system executes classification of student actions, minimizing latency while maximizing processing responsiveness. Captured data is aggregated into a management dashboard that provides class-level engagement indicators based on predefined behavioral metrics. Results demonstrate that the 5G-enabled infrastructure ensures reliable data transmission and efficient remote management, providing a scalable proof-of-concept for intelligent educational environments.