Integrated AI Framework for Student Attendance Tracking and Academic Performance Monitoring with AR-Based Data Visualization

We propose a novel AI-driven framework for fully automated student attendance and performance monitoring, enhanced with real-time augmented reality (AR) visualization for instructors. The system leverages facial recognition to mark classroom attendance instantly upon student entry, eliminating manual sign-in. Simultaneously, it performs real-time engagement analysis by tracking eye gaze, head pose, device usage and posture to estimate each student’s focus and distraction level. Student academic records and sports performance data are integrated to provide a holistic view of each learner’s progress. A cognitive energy metric is computed to detect mental fatigue or overload. All insights are delivered via live AR overlays on teachers’ smart glasses, allowing immediate instructor feedback. This hybrid approach aims to improve accuracy of attendance and to alert educators to disengagement before it affects learning. Experimental evaluation indicates that the proposed system achieves high recognition accuracy and provides timely alerts for disengaged students, supporting more effective classroom monitoring.