AI-Based Online Examination Proctoring System

Online examinations have become a cornerstone of modern education, yet they are increasingly vulnerable to

sophisticated threats such as cheating, impersonation, and the illicit use of AI-generated content. Traditional proctoring

solutions often suffer from high human dependency and significant privacy risks due to centralized data storage. To address

these challenges, this paper proposes a next-generation, AI-based privacy-preserving online examination proctoring system.

The framework utilizes Federated Learning to ensure that sensitive student data is processed locally, thereby maintaining strict

data confidentiality.

Integrity is further reinforced through a multi-layered monitoring approach: remote Photoplethysmography (rPPG) is

employed to analyze physiological stress signals via a webcam to detect suspicious behavior , while Keystroke Dynamics

provides continuous identity verification based on unique typing patterns. Additionally, the system incorporates a voice-based

AI Proctor Agent for real-time automated warnings and Large Language Model (LLM)-based plagiarism detection to identify

AI-generated or copied answers in real-time. By integrating these advanced AI and machine learning techniques, the proposed

system offers a secure, fair, and scalable solution that reduces the burden of manual invigilation while ensuring the overall

reliability of the examination environment.