Online Examination System with AI Proctoring – Complete Project Guide

Traditional online exams often face major challenges like cheating, impersonation, and lack of real-time supervision. Manual invigilation over video calls is limited in scale, time-consuming, and often ineffective for maintaining academic integrity.

Imagine a system that not only delivers exams online but also automatically monitors candidates through AI-powered proctoring — detecting suspicious behavior, multiple faces, phone usage, or eye movements. The platform ensures a secure, scalable, and unbiased examination process without needing human invigilators at every session.

The system architecture uses webcam feeds, screen recording, AI algorithms, and secure browsers to ensure the integrity of online examinations:

• Students authenticate their identity via facial recognition before the exam begins.
• Throughout the exam, AI monitors webcam feeds for anomalies such as unauthorized devices or additional faces.
• Screen activity is tracked for suspicious behavior like switching tabs or opening new windows.
• Real-time alerts and automatic recordings are generated for any rule violations.
• Exam supervisors can review flagged events and make final evaluations.

You can implement this system using the following technologies:

• Frontend: React.js, Next.js, Flutter (for mobile)
• Backend: Node.js (Express), Django, or Firebase Functions
• AI Proctoring Libraries: TensorFlow.js, OpenCV, or DeepFace
• Database: Firebase Firestore, PostgreSQL, or MongoDB
• Authentication: OAuth2, Biometric verification
• Screen and Webcam Access: WebRTC, MediaDevices API

• WebRTC Real-Time Communication Overview
• MDN MediaDevices API Documentation
• TensorFlow.js for Browser-Based AI
• DeepFace – Lightweight Face Recognition Library
• Firebase Firestore Official Documentation