Autonomous Proctoring Software: A Comprehensive Framework for Ensuring Academic Integrity in Remote Examinations
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Abstract— A novel autonomous proctoring software architecture that guarantees academic integrity throughout online tests. Strong, scalable, and minimally intrusive proctoring systems are more important than ever in the context of growing distant learning. Our method combines behavior analysis, biometric identification, and sophisticated machine learning algorithms to track candidate activity and identify anomalous trends instantly. According to experimental results, the suggested solution successfully balances privacy and usability while identifying possibly fraudulent actions. A thorough performance study and a detailed architectural design provide more details about the framework.
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