How to Detect AI Cheating on Zoom, Teams, and Google Meet Interviews

A stealth AI copilot works the same way regardless of which video call platform an interview happens on — it doesn't care whether the call is Zoom, Teams, or Meet. What differs across platforms is what's actually visible to you as the interviewer, and a few practical details worth knowing for each.

Zoom

Zoom's gallery and speaker views make it slightly easier to notice a candidate's eyes repeatedly drifting to one fixed spot off-camera — a second monitor, most commonly — since the video window's position relative to a typical desk setup is fairly predictable. Zoom's own live transcription (where enabled) can also be a source of raw text a copilot reads from, which is worth knowing if your organization has that feature turned on for interviews specifically. Zoom's "Hide Non-Video Participants" and spotlight settings can also affect how much of a candidate's environment you actually see during screen-share-heavy technical rounds — worth checking your default settings if technical screens are a big part of your process.

Microsoft Teams

Teams interviews are more likely to happen in a corporate environment with managed devices, which cuts both ways: it's slightly harder for a candidate to run extra software undetected on a locked-down company laptop, but external candidates joining from their own device have exactly the same opportunity as on any other platform. Together mode and larger gallery layouts in Teams can also make subtle eye-movement tells a little easier to miss if you're watching several tiles at once. Teams' built-in live captions, similarly to Zoom's transcription, are a source of real-time text that a copilot could theoretically read from if enabled during an interview.

Google Meet

Meet's simpler, more minimal interface means there's less visual furniture around the video tile — which can make a consistent off-camera glance pattern somewhat more noticeable by contrast, but Meet also has less built-in tooling (compared to Zoom or Teams) for an interviewer to lean on for anything beyond the raw call itself. Meet's live captions are on-device rather than server-processed in some configurations, which is a minor technical distinction but doesn't meaningfully change what a candidate could or couldn't access during the call.

What's actually platform-independent

None of the above changes the fundamental signal that matters most: what the candidate is actually saying, and how they're saying it. Cadence tells — missing filler words, a sudden shift to unnaturally polished phrasing, a flawless answer with zero hesitation on a question they didn't expect — show up identically whether the call is on Zoom, Teams, or Meet, because they're a property of the answer itself, not the platform delivering the video.

A practical checklist regardless of platform

• Confirm whether live captions/transcription are enabled for the call, and whether that's a feature you want on for interviews at all.
• Don't rely on visual monitoring alone — a candidate with a well-positioned second monitor can defeat most visual checks on any platform.
• Standardize your process across platforms rather than trusting one more than another — the underlying risk (a stealth copilot reading the audio) is identical on all three.
• If your organization allows candidates to choose the platform, make sure your detection approach doesn't quietly depend on features specific to just one of them.

Why detection built into the meeting, not the platform, is what actually works

A detection approach that has to be configured differently per video platform is a detection approach that's fragile by design. The more robust version reads the transcript itself — something every major platform can provide access to — rather than trying to watch platform-specific visual cues that vary from one tool to the next.

Candit's bot joins Zoom, Google Meet, Microsoft Teams, Slack and Webex calls the same way on all of them, reading the live transcript in the background regardless of which platform the interview happens to be on, and flags an AI-assisted answer the moment it crosses the threshold — per speaker, with the evidence behind it, with nothing platform-specific to configure. A flag also suggests a follow-up to ask right then (Counter AI), and the AI use report after the call shows where and how AI help showed.

It's also worth being clear about why this still works against a tool that hasn't been named yet: Candit doesn't try to lock down a candidate's device or browser the way older proctoring tools do — there's no environment to escape, because the signal isn't what's on a screen, it's how the answer was actually delivered, read live from the transcript and audio as the interview happens. That's ongoing work, not a one-time build: reverse-engineering how candidates actually use these tools is retested on a roughly weekly basis as new copilots appear.

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