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@article{174024,
        author = {Harshal Jain and Garv Kamra and Arun Manas Divi and Garvit Maheshwari and Mohammad Atif and Ashvini Alashetty},
        title = {AI-Based Classroom Monitoring and Attendance System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2523-2530},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174024},
        abstract = {This paper describes an artificial intelligent attendance system for education that includes behavioral tracking and distractions interruptions. Inadequate student attendance accounts derive from the limits of traditional roll call systems in measuring student engagement. This proposal integrates motion detection, sleep recognition, facial recognition, and mobile phone usage monitoring to provide real-time focus tracking of students. The system was implemented on a Raspberry Pi and uses OpenCV and machine learning for accurate behavior assessment and recognition. It also correlates students’ engagement to the instructional activities being performed in the class to determine teacher effectiveness. The real-time monitoring of data allows the decision-makers and educators to target and improve the teaching approaches to better serve the student needs and outcomes. When traditional attendance monitoring is replaced by AI driven real-time classroom monitoring, attendance grows, learning achievements are reached, and distractions are minimized.},
        keywords = {AI-Based Attendance, Face Recognition, Behavioral Monitoring, Student Engagement Analysis, Teacher Efficiency Evaluation, Phone Usage Detection, Sleep Detection in Classrooms, Real-Time Classroom Analytics, Automated Attendance System, Machine Learning in Education},
        month = {March},
        }