Identifying the Correlation Between Online Exam Answer Trajectory and Test Behavior Based on Artificial Intelligence and Eye Movement Detection Technology

COVID-19 has brought many challenges to higher education, such as remote cheating derived from remote assessment, students are easy to obtain cheating channels and resources, and the burden of remote invigilators being large, all of which make the fairness of remote assessment highly critical question. In order to improve the problems existing in remote assessment, this paper proposes a deep learning artificial intelligence proctoring system (AIPS), which detects whether there is any abnormality in the answer through the webcam gaze and eye tracking. Behavior, combined with Long Short Term Memory (LSTM) to design a deep learning model that can be used to detect whether there is an abnormal answer in real-ter the test is over, a report can be output, including eye movement answer hot spots, test question answer order. This paper develops AIPS which is expected to assist invigilators to find candidates with abnormal answers through artificial intelligence assistance, so as to improve the fairness of online assessment.