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AI based test monitoring uses machine learning models, rather than a person watching a video feed, to flag suspicious behavior during an online assessment in real time. It exists because the volume and sophistication of cheating attempts has outpaced what human reviewers can realistically catch on their own.
CodeSignal’s February 2026 analysis of its own proctored assessments puts the scale of the problem in sharp relief: cheating and fraud attempt rates more than doubled in 2025, rising from 16 percent in 2024 to 35 percent, and entry level roles were hit hardest, nearly tripling from 15 percent to 40 percent over the same period. The rate also varies sharply by region, reaching 48 percent in Asia Pacific compared to 27 percent in North America, which matters for any hiring program running assessments across multiple geographies.
AI based test monitoring typically combines four signal types rather than relying on any single one:
None of these signals alone proves cheating. Credible systems combine several signals and route ambiguous cases to a human for a final decision, rather than automatically failing a candidate on a single flag.
A human proctor watching a video feed, or reviewing a recording after the fact, can reasonably track one or two obvious cues: someone visibly reading off screen, a second person in frame. What a human proctor cannot do at scale is continuously analyze typing cadence against a candidate’s own earlier baseline, cross reference gaze patterns across an entire session, or flag subtle score inflation compared to a candidate’s own historical pattern across multiple assessments.
This is precisely the kind of pattern detection AI systems are suited for, and precisely what gets missed when the same reviewer is watching dozens of sessions in parallel.
This is worth stating honestly rather than overselling the technology: research on assessment integrity has found that a majority of cheaters still pass undetected even where behavioral detection systems are in place, and that every individual detection method in production today, process name scanning, keystroke dynamics, gaze tracking, perplexity scoring, has a documented, working bypass technique.
This does not make AI monitoring useless. It makes single method AI monitoring insufficient on its own. The practical implication is that AI based test monitoring works best as one layer inside a broader online exam monitoring strategy, combined with identity verification and, for higher stakes assessments, targeted human review rather than full automation. See how AI proctoring compares to human proctoring for a deeper look at where each approach is stronger.
| Factor | AI Based Test Monitoring | Human Proctoring |
|---|---|---|
| Scale | Monitors thousands of sessions in parallel | Limited to a handful of sessions per reviewer |
| Consistency | Applies the same thresholds every time | Varies by reviewer attention and fatigue |
| Pattern detection | Flags cadence and score pattern shifts across sessions | Rarely tracked across sessions |
| Ambiguous cases | Needs human review for context | Handles nuance and context directly |
| Cost at volume | Low marginal cost per candidate | High marginal cost per candidate |
Treat an AI flag as a prompt for human review, not an automatic disqualification, since false positives are possible and a candidate deserves a fair look before a flag costs them an opportunity. Pair AI based monitoring with identity verification at the start of the process, since a monitored session is only as trustworthy as the confirmation that the right person started it.
And treat detection methods as something that needs to keep evolving, since the cheating tactics they are built to catch are evolving quickly too.
AI based test monitoring is not a solved problem, it is a moving target that is currently ahead of most cheating tactics but not ahead of all of them. Used as one layer among several, alongside AI proctoring software built for hiring specifically rather than a single silver bullet, it meaningfully raises the cost and difficulty of cheating even where it cannot catch everything.
AI based test monitoring uses machine learning models to flag suspicious behavior during an online assessment in real time, tracking signals like facial mismatches, gaze patterns, typing rhythm, and browser activity rather than relying on a person watching a video feed.
AI detection is improving but imperfect. Research has found that a majority of cheaters can still pass undetected even with behavioral detection systems active, which is why credible implementations pair AI flags with human review rather than fully automated decisions.
Yes. Documented bypass techniques exist for individual detection methods used in isolation, including process scanning, keystroke dynamics, gaze tracking, and perplexity scoring, which is why layering multiple methods together produces more reliable results than any single method alone.
It typically tracks facial identity match, gaze and attention patterns, typing rhythm and cadence, and browser or process activity such as tab switching or remote access software running in the background.
AI monitoring can track more signals continuously and at greater scale than a human watching video feeds, but it works best combined with human review for ambiguous cases rather than as a full replacement for human judgment.
Yes in most jurisdictions, though a growing number of state laws require advance disclosure, consent, and in some cases a bias audit before using AI to evaluate candidates. See our guide to ID verify compliance for the current legal landscape.
AI based monitoring has a low marginal cost per candidate since the same system scales across thousands of sessions, while human proctoring cost grows directly with headcount, which is why most high volume hiring programs use AI as the first layer and reserve human review for flagged sessions only.
Yes. AI based test monitoring is built specifically for remote and asynchronous formats where no live human proctor is present, using recorded signals like webcam feed, keystroke pattern, and browser activity to flag issues for later or real time review.
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