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<p>Exams are inherent in building confidence and appraising an individual in schools and colleges. However, supervising an exam is a tedious process given the large number of students in a class. This constant supervision is necessary to prevent students from cheating via copying, browsing, talking, or electronic means, thereby harming the integrity and authenticity of the examination. Technology has revolutionized how proctoring is undertaken, making this process more efficient by transitioning to online proctoring systems from traditional ones. Due to COVID-19, this online proctoring system has significantly shifted to utilize technologies such as Artificial Intelligence (AI) and Machine Learning (ML). Existing surveys focus on behavioral, pedagogical, and governance levels; however, there is a lack of discussion on applications, technologies, and associated challenges. This paper analyzes the challenges associated with conventional, online, automated, and AI-based proctoring systems and identifies the research gaps based on different applications and their impact on education and academia. Moreover, the paper provides conceptual scenarios related to automated online proctoring systems, insights into various categories, and the challenges associated with the survey, which will act as a boon to future researchers.</p>

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A comprehensive review of the changing landscape of academic dishonesty in automated proctoring in the era of artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

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  • Manit Malhotra,
  • Indu Chhabra

摘要

Exams are inherent in building confidence and appraising an individual in schools and colleges. However, supervising an exam is a tedious process given the large number of students in a class. This constant supervision is necessary to prevent students from cheating via copying, browsing, talking, or electronic means, thereby harming the integrity and authenticity of the examination. Technology has revolutionized how proctoring is undertaken, making this process more efficient by transitioning to online proctoring systems from traditional ones. Due to COVID-19, this online proctoring system has significantly shifted to utilize technologies such as Artificial Intelligence (AI) and Machine Learning (ML). Existing surveys focus on behavioral, pedagogical, and governance levels; however, there is a lack of discussion on applications, technologies, and associated challenges. This paper analyzes the challenges associated with conventional, online, automated, and AI-based proctoring systems and identifies the research gaps based on different applications and their impact on education and academia. Moreover, the paper provides conceptual scenarios related to automated online proctoring systems, insights into various categories, and the challenges associated with the survey, which will act as a boon to future researchers.