TruthEye graduation project
11 Jul 2026
TruthEye is a graduation project that aims to develop a smart platform for managing and monitoring online exams using artificial intelligence and computer vision technologies. Its goal is to enhance exam integrity and support the digital transformation of educational institutions.
The platform offers a comprehensive system for managing the entire exam lifecycle, from creating and scheduling exams to verifying student identity using Face Recognition and Liveness Detection technologies.
The platform relies on real-time monitoring during exams. It analyzes video using artificial intelligence to detect any behavior that could compromise exam integrity. This is achieved through eye tracking and gaze detection to identify attempts to look away from the screen, detect multiple people in front of the camera, and identify mobile phones or unauthorized objects using smart detection technologies. Additionally, an Access Control Detector detects the operation of remote control programs or unauthorized applications—such as AnyDesk—during the exam, sending immediate alerts to the proctor and recording all violations.
The platform also provides a Risk Score system to assess the severity of each violation and take appropriate action according to exam regulations. It features a real-time dashboard for proctors to monitor all exam sessions, an appeals system that allows students to review recorded violations, and a multi-role permissions system that ensures each user can access only their assigned tasks.
The project was developed using ASP.NET Core and React.js, while the AI services were built using Python and the FastAPI framework. MediaPipe, InsightFace, and YOLO models were employed to achieve accurate, real-time visual analysis.
The TruthEye project exemplifies the practical application of AI technologies in developing e-exam systems. It offers a solution that combines accuracy, security, and ease of management, contributing to the future of digital education and enhancing trust in electronic assessment systems.
Team:
- Aya Mahmoud Hussein
- Yousra Bahaa Sayed
- Reham Khaled Abdelhamid Mohamed
- Soha Elmoataz Bellah Awais El-Qarni Mohamed
- Monica Hany
- Merola Magdy Bishry
Supervised by:
- Professor Dr. Mohamed Sayed Qaid
- Engineer Ehab Ibrahim
Faculty of Computers and Artificial Intelligence – Beni Suef University