Case Study on Emotion-Aware Smart Classroom for Adaptive Teaching Using Artificial Intelligence

Traditional classroom teaching methods often fail to consider students’ emotional and engagement levels, which directly affect learning outcomes. This paper proposes an Emotion-Aware Smart Classroom System that utilizes Artificial Intelligence and IoT technologies to detect students’ emotional states in real time and adapt teaching strategies accordingly. The system employs a deep learning–based facial emotion recognition model to classify emotions such as happiness, neutrality, confusion, boredom, and stress. Based on the detected emotional patterns, adaptive feedback is provided to instructors to modify teaching pace, content delivery, or interaction methods. Experimental results demonstrate improved student engagement and learning effectiveness compared to conventional classrooms. The proposed system offers a scalable, privacy-aware, and intelligent solution for next-generation smart education environments.