Published Online:June 2026
Product Name:The IUP Journal of Information Technology
Product Type:Article
Product Code:IJIT010626
DOI:10.71329/IUPJIT/2026.22.2.6-31
Author Name:Ambika B Sajjan, Pooja R Y, Khushi Jain and Ananta Charan Ojha
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:6-31
The rapid growth of online learning platforms induces remote examination systems. However, remote online examinations face significant challenges in maintaining academic integrity. Although automated proctoring technologies have emerged, many existing systems rely on single-modality monitoring techniques such as webcam observation and browser activity tracking, which provide only limited insights into candidate behavior. To address these limitations, this paper proposes a multimodal artificial intelligence (AI)-based proctoring framework for remote examination that integrates computer vision-based behavioral monitoring with system-level activity analysis. The proposed framework adopts layered architecture to facilitate multimodal data acquisition, preprocessing, behavioral analysis, event detection, decision intelligence, and secure evidence management. The system analyzes behavioral indicators such as facial movements, gaze direction, head pose, and digital interaction patterns to identify potentially suspicious activities during examinations. A structured workflow and behavioral detection pipeline are introduced to coordinate data collection, multimodal behavioral analysis, and risk assessment. The framework further incorporates a risk score computation that evaluates suspicious behavioral events and supports interpretable decision making for human proctors. By combining multimodal monitoring with explainable decision making, the proposed approach aims to enhance the reliability, scalability, and transparency of remote examination supervision in higher education environments.
The rapid expansion of online and blended learning environments has significantly transformed the landscape of higher education today. Digital learning platforms and Learning Management Systems (LMS) now support large-scale remote teaching, enabling universities to reach geographically dispersed learners and support flexible learning models.