AI-Driven Online Exam Proctoring: An Enhanced Machine Learning Approach

The proliferation of an online learning environment has opened up tremendous potential in the sphere of education, but has also posed significant problems to examination integrity. Conventional services of online proctoring, i.e., manual webcam supervision and lockdown browsers, failed to provide fairness since they were either inefficient or rather effortless to manipulate. This paper introduces an AI-based online exam proctoring (OEP) model that combines both visual and audio channels to identify cheating behavior, such as reading notes or using cell phones, muttering, or turning away the view on the examination. The research builds on a previously established framework that uses Support Vector Machines (SVMs), and tries different alternatives by using Random Forests (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models. A comparative analysis shows that the baseline system displayed an average True Detection Rate (TDR) of 87% under 2% False Alarm Rate (FAR) whereas the enhanced model with CNN-based visual processing and LSTM educated speech detection produced an improved system performance to 94% TDR at the same FAR limitation. The results indicate the potential of advanced ML to circumvent the drawbacks of earlier solutions and point to a way forward that results in scalable, fair, and privacy-sensitive proctoring solutions.