Development and Evaluation of a Real-Time Emotion Detection System to Enhance Student Interaction

This research explores the development of a real-time emotion detection system to improve engage - ment in online learning. The system uses Convolutional Neural Networks (CNN) to identify five emotions: happy, sad, angry, surprised, and neutral via webcam during virtual classes. Tested with 30 students in an Artificial Intelligence course, it achieved 86.4% accuracy, excelling in detecting happy and neutral states. Instructors used emotional feedback to adapt teaching dynamically, enhancing learning experiences and satisfaction. Feedback showed that 88% of students felt more motivated and engaged. This study high - lights the potential of emotion-based tools in bridging gaps between online and traditional education.