Eagle Eye: Enhancing Online Exam Proctoring Through AI-Powered Eye Gaze Detection

With the significant rise in online examinations, the demand for proctors has grown exponentially, leading to resource constraints. Unlike offline exams with a few invigilators overseeing large groups of students, online exams require individual monitoring to uphold the code of conduct. However, the prevalence of unfair practices among examinees in online exams remains notably higher than in offline settings, resulting in an extensive, tiresome, and inefficient process. To address these challenges, we present “Eagle Eye”, a coherent and efficient system that employs eye gaze detection with machine learning and artificial intelligence. During the exam setup, examinees undergo a calibration test to establish a designated border-box area for eye movement testing. Data gathered from this detection enables classification of examinee behaviour as fraudulent or fair based on their gaze within or outside the box. When fraud is detected, alerts are sent to both the examiner and examinee, allowing timely actions as needed. To ensure accurate predictions, we have curated a bespoke dataset with the help of volunteers, providing unfiltered and authentic samples for training. The implementation of Eagle Eye seeks to enhance online exam integrity and streamline the proctoring process.