Emotion-Aware Classroom Quality Assessment Leveraging IoT-Based Real-Time Student Monitoring

2026-03-17

AI Summary

This study addresses the limited teacher–student interaction and the difficulty of real-time perception of students’ emotional states and engagement levels in large-classroom settings. To this end, we propose the first IoT-driven multi-agent affective computing framework tailored for real-world classrooms, integrating a lightweight face detection and emotion classification model that enables low-latency, high-throughput processing on edge devices—capable of simultaneously detecting 50 faces at 25 FPS. The system achieves 88% accuracy in classifying classroom engagement states and introduces the “Classroom Emotion Dataset,” comprising 1,500 images and 300 video clips. Validated across three distinct school environments, the framework effectively supports teachers in dynamically adapting instructional strategies, receiving positive feedback from educators, students, and parents alike.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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. Tailored for IoT devices, the system addresses load balancing and latency challenges through efficient real-time processing. Field testing was conducted across three educational institutions in a large metropolitan area: a primary school (hereafter school A), a secondary school (school B), and a high school (school C). The system demonstrated robust performance, detecting up to 50 faces at 25 FPS and achieving 88% overall accuracy in classifying classroom engagement states. Implementation results showed positive outcomes, with favorable feedback from students, teachers, and parents regarding improved classroom interaction and teaching adaptation. Key contributions of this research include establishing a practical, IoT-based framework for emotion-aware learning environments and introducing the 'Classroom Emotion Dataset' to facilitate further validation and research.

Problem

• classroom quality assessment
• student emotion monitoring
• real-time engagement
• large-class teaching
• affective computing

Innovation

• emotion-aware learning
• IoT-based monitoring
• real-time affective computing
• Classroom Emotion Dataset
• multi-agent framework