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A Comprehensive Review of the Changing Landscape of Academic Dishonesty in Automated Proctoring in the Era of Artificial Intelligence

● high · 2026-08-20 · remote-proctoring, academic-integrity, automated-proctoring, online-education, higher-ed, covid-19, machine-learning, privacy

Synthesis: Malhotra & Chhabra (2026) comprehensively review the landscape of academic dishonesty across conventional, online, automated, and AI-based proctoring systems, catalyzed by COVID-19's shift to remote exams. They trace the transition from offline invigilation to online and AI/ML proctoring, document the cheating methods AI systems must counter (identity spoofing, browser/device use, copy-paste), and identify the practical, infrastructure, and psychological challenges — anxiety, lack of proficiency, cost, and connectivity — that shape proctoring adoption and effectiveness.

Key Findings

  • Widespread cheating motivates proctoring. Studies cited report that ~37.8% of college and ~41.8% of high-school students admit to cheating or misconduct during exams — a key driver of monitoring investment.
  • Transition from offline to online/AI proctoring. COVID-19 accelerated the shift from physical invigilation to online proctoring (human proctors via webcam/control center) and then to AI/ML-driven automated systems; common platforms include ProctorU and Kryterion.
  • Cheating methods AI must counter. Identity spoofing (masking a face via photographs or video to impersonate another), browser/tab use to search online, and copy-paste from books, phones, or cheat sheets.
  • Challenges of online proctoring. Test-taker anxiety (especially for users not proficient with online tools), proctor/test-taker proficiency gaps causing false malpractice accusations, and infrastructure requirements (webcams, microphones, internet) that are not affordable or available to all.
  • AI/ML proctoring captures facial expressions and emotions through imaging systems, enabling richer behavioral analysis but raising cost and infrastructure demands.

Study Design & Method

A comprehensive thematic review of proctoring systems (conventional, online, automated, AI-based), analyzing applications, technologies, challenges, and research gaps across eight research questions covering features, challenges, countermeasures, and future trends. It synthesizes the literature on proctoring's evolution, categories, and associated challenges to guide future research.

Implications for AI in Education

Remote and automated proctoring addresses the integrity needs of online and distance assessment, but its effectiveness is bounded by infrastructure equity, psychological burden, and the risk of false accusations. Institutions adopting Remote Proctoring must pair AI monitoring with accessible alternatives, clear communication, and support for test-taker anxiety, and should weigh it against assessment redesign that reduces reliance on surveillance.

Connected Concepts

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Citation

Malhotra, M., & Chhabra, I. (2026). A comprehensive review of the changing landscape of academic dishonesty in automated proctoring in the era of artificial intelligence. Discover Education, 5, 236.