VIRTUAL VIGILANCE: AI-BASED PROCTORING FRAMEWORK FOR E-LEARNING ENVIRONMENTS

Abstract

The rapid adoption of online education has created a pressing need for secure and reliable assessment mechanisms that uphold academic integrity. Traditional proctoring methods are often intrusive, resource-intensive, and limited in scalability, making them less effective in the evolving digital learning landscape. This study introduces Virtual Vigilance, an AI-based proctoring framework designed to ensure fairness, security, and transparency in online examinations. The framework leverages advanced machine learning and computer vision techniques to detect anomalies such as identity fraud, unauthorized resource usage, and suspicious behavioral patterns in real time. By integrating natural language processing and facial recognition, the system can identify potential violations while minimizing false positives and reducing unnecessary intrusions on student privacy. Furthermore, the framework incorporates cloud-driven scalability, enabling seamless deployment across diverse e-learning platforms. Experimental evaluations demonstrate that the proposed solution significantly enhances detection accuracy and operational efficiency compared to conventional proctoring approaches. Virtual Vigilance thus offers a robust, ethical, and intelligent pathway toward reinforcing trust in online education systems