AI Proctoring: Capabilities, Shortcomings, and How To Fix Them

AI proctoring was supposed to make online exams and hiring assessments more secure, but many educators and hiring professionals remain skeptical. Reports of false flags, missed cheating attempts, and limited visibility into real test conditions have left many questioning whether AI is actually an improvement (Tweissi, Al Etaiwi, & Al Eisawi, 2022). 

Most proctoring solutions today rely on AI to detect suspicious behavior, but they often fail in key areas. Below, we’ll break down what AI proctoring does well, where it falls short, and how to overcome these limitations.

What AI Proctoring Can Do

AI proctoring solutions use machine learning models to detect unusual test-taking behaviors. This typically includes:

• Facial recognition – Ensuring the test-taker is the correct person.

• Eye-tracking and head movement analysis – Looking for signs of distraction or assistance.

• Browser activity monitoring – Detecting tab switching or unauthorized applications.

• Audio detection – Flagging speech or background voices.
  
  

In theory, this should create a secure testing environment. But in practice, AI proctoring is riddled with issues.

Where AI Proctoring Falls Short

1. False Positives and Misinterpretations

AI proctoring tools frequently flag normal behavior as cheating. A study testing AI proctoring software found a 30% incorrect decision rate when compared to human proctors​. Movements as simple as adjusting glasses or reading a question out loud can be mistaken for an integrity violation (Tweissi et al., 2022). These inaccuracies lead to unnecessary stress for test-takers and extra work for administrators.

2. Limited Visibility into the Test Environment

Most proctoring solutions focus on webcam feeds and screen activity, but they can’t see what’s happening off-screen. A test-taker can easily use a second device or have unauthorized materials just outside the camera’s view. Some students even manipulate their internet connection to pause video feeds, disrupting AI’s ability to monitor consistently (Nigam et al., 2021)​.

3. Inability to Differentiate Between AI Assistance and Genuine Work

The rise of generative AI has made traditional proctoring methods ineffective. Many tools struggle to detect AI-generated responses, especially in written or coding exams. If an applicant or student quickly pastes AI-generated answers, most systems won’t catch it (Tweissi et al., 2022). AI proctoring tools were not designed with this challenge in mind, making them outdated in today’s test-taking environment.

4. Privacy and Trust Issues

AI proctoring has raised concerns about privacy, particularly regarding facial recognition and data storage. Some institutions have faced backlash for invasive monitoring practices that feel more like surveillance than assessment security (Nigam et al., 2021)​. Educators and hiring managers want integrity in testing, but not at the cost of ethical concerns.

How To Solve The Issues

Most AI proctoring solutions are flawed because they rely too heavily on surface-level monitoring. We take a different approach, fixing these problems without creating new ones.

• No More Guesswork – We don’t rely on AI alone to flag behaviors without context. Our system is designed to assess intent and patterns, reducing false positives while catching real violations.

• Full Visibility Without Overreach – We ensure that test conditions remain controlled, eliminating blind spots without intrusive monitoring.

• AI Chating Detection – Unlike most proctoring solutions, we account for the presence of generative AI tools and unauthorized assistance in real-time.

• Adaptable Proctoring – Whether for exams, hiring interviews, or admissions assessments, our system adjusts to the testing format without one-size-fits-all restrictions.
  
  

AI proctoring has potential, but most existing solutions don’t live up to expectations. Proctaroo was built to solve these problems, ensuring fair, accurate, and efficient proctoring without unnecessary complexity.

Want to see it in action?

References

Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021). A systematic review on AI proctoring systems: Past, present, and future. Education and Information Technologies, 26, 6421–6445. https://doi.org/10.1007/s10639-021-10597-x

Tweissi, A., Al Etaiwi, W., & Al Eisawi, D. (2022). The accuracy of AI automatic proctoring in online exams. The Electronic Journal of e-Learning, 20(4), 419–435.