Enhance Massive Open Online Courses Integrity: AI for Exam Proctoring

Massive Open Online Courses (MOOCs) are growing quickly, but it's challenging to ensure academic integrity during remote exams with many participants. Existing approaches to supervising students online have scalability, accuracy, and integration limitations. This paper proposes a scalable, accurate AI exam proctoring module compatible with MOOCs to address this issue. Our approach includes an AI server that handles image processing and coordinates cheating detection services. Another server uses Triton to analyze student image feeds quickly. It runs optimized deep learning models, such as face recognition. There is also an integrated MOOC client to capture, compress, and transmit images. The main innovations are the asynchronous AI server for handling multiple tasks simultaneously, efficient deep learning pipelines that use fewer computing resources, and the integration of inference pipelines into Triton for faster processing. The integration of the AI module into the MOOC has been successful. The system can monitor multiple test-takers at the same time and accurately detect any potential cheating. Evaluations showed high accuracy of different AI models.