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 video processing and coordinates cheating detection services. Another server uses Triton to analyze student video feeds quickly. It runs optimized deep learning models, such as face recognition. There is also an integrated MOOC client to capture, compress, and transmit video. 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 the high accuracy on different AI models
Keyword
AI, face recognition, phone detection, face pose estimation, online proctoring, MOOCs platform
