A real-time attention monitoring system in classrooms focuses on understanding how attentive students are during lectures using modern technology. It combines computer vision and machine learning to observe student behavior without interrupting the class. Cameras capture live video, and the system analyzes facial expressions, eye movement, and posture to detect attention levels. Instead of manual observation, the system automatically identifies whether students are focused or distracted. The collected data is processed instantly, allowing teachers to get real-time feedback on classroom engagement. This helps in identifying students who may be struggling to concentrate. A dashboard provides a simple visualization of attention levels, making it easier for teachers to adjust their teaching methods. At the same time, privacy and data protection are carefully considered while designing the system. It can be implemented in different classroom sizes and supports smart classroom environments. Overall, this system helps create a more interactive and effective learning experience by bridging the gap between teaching and student engagement.
Phad et al. (2026) studied this question.