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Artificial Intelligence in the Educational System: Theoretical Perspectives and a Practical Application for Cheating Detection in Examination Halls"

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This research demonstrates an AI-based system for detecting cheating during exams, highlighting its implications for academic integrity.

The system achieved over 90% accuracy in detecting cheating cases in real time, using facial and eye movement analysis.
Haar Cascade algorithms, optical flow, and SVM classification were utilized for tracking facial features and identifying suspicious behavior.
Challenges such as poor lighting were identified, suggesting the need for improvements in the system’s design and functionality.
Future development may include deep learning techniques and multiple cameras to enhance performance and effectiveness.

Betti et al. (2025) studied this question.

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