AI RESEARCH
Emotion-Aware Classroom Quality Assessment Leveraging IoT-Based Real-Time Student Monitoring
arXiv CS.CV
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ArXi:2603.16719v1 Announce Type: new This study presents high-throughput, real-time multi-agent affective computing framework designed to enhance classroom learning through emotional state monitoring. As large classroom sizes and limited teacher student interaction increasingly challenge educators, there is a growing need for scalable, data-driven tools capable of capturing students' emotional and engagement patterns in real time. The system was evaluated using the Classroom Emotion Dataset, consisting of 1,500 labeled images and 300 classroom detection videos.