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VOL. 10, ISSUE 3 (2025)
X`AI-based student behavior analysis for classroom discipline and engagement monitoring
Authors
Thakare Mahendra Vishwanath Vis, Tejas Somnath Kshirsagar
Abstract
Artificial Intelligence (AI) has
revolutionized education by enabling real-time monitoring and analysis of
student behavior to enhance classroom discipline and engagement. This paper
presents an AI-driven system that utilizes computer vision, natural language
processing (NLP), and machine learning algorithms to track student activities,
facial expressions, posture, and speech patterns to assess attentiveness,
participation, and behavioral anomalies. The proposed framework integrates
automated data collection and predictive analytics to provide teachers with
actionable insights, enabling proactive intervention to improve learning
outcomes. By leveraging deep learning models for facial emotion recognition and
object detection, the system classifies student engagement levels while
detecting instances of inattention, misconduct, or disengagement. Additionally,
NLPbased sentiment analysis of classroom interactions helps evaluate
communication effectiveness and peer engagement. The research discusses the
ethical implications, data privacy concerns, and accuracy of AI-based behavior
analysis while proposing solutions to ensure fairness and reliability.
Experimental results demonstrate the effectiveness of the system in enhancing
classroom management by assisting educators in making data-driven decisions to
foster an inclusive and productive learning environment.
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Pages:189-192
How to cite this article:
Thakare Mahendra Vishwanath Vis, Tejas Somnath Kshirsagar "X`AI-based student behavior analysis for classroom discipline and engagement monitoring". International Journal of Advanced Scientific Research, Vol 10, Issue 3, 2025, Pages 189-192
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