Interview Cheating With AI: Keep Assessments Honest

Interview cheating with AI is no longer a fringe worry. When a candidate can paste a coding prompt into a model and get a working answer in seconds, the traditional take-home test and the unwatched online assessment lose a lot of their meaning. The reaction from some teams — lockdown browsers, webcam monitoring, keystroke logging — often does more damage to the honest majority than it does to cheaters.

There is a calmer path. You cannot make cheating impossible, but you can design assessments where AI assistance either does not help much or leaves a trace you can see.

Design questions AI cannot answer for them

The single most effective defence is question design. Generic problems with a single correct answer are exactly what models are best at. Questions rooted in their specific experience are not.

• Ask them to walk through a real decision they made and what they would change.
• Tie questions to the actual job and the gaps in their background, not a textbook.
• Favour "why" and "what would you do when X breaks" over "write a function that does Y."

Talent Tick generates 10 personalised questions per candidate — 5 theory and 5 coding — targeting the specific gaps in their score against the rubric. Personalised, gap-targeted questions are harder to outsource than a generic problem set, because they assume context the candidate is supposed to already have.

Watch behaviour, not faces

You do not need a webcam staring at someone to spot the patterns that matter. The useful signals are behavioural and far less invasive:

1. Tab switching. Frequent switches away during a timed question can indicate someone is consulting another tool.
2. Paste events. A large block of code pasted in one motion is different from code typed out.
3. Time per question. Suspiciously fast, perfect answers to hard problems are worth a second look.

None of these prove cheating on their own. They tell you where to ask a sharper follow-up question.

Talent Tick's async self-interview tracks exactly these — tab switches, paste events, and time per question — so a reviewer gets context, not accusations.

Use async honestly

Async self-interviews are convenient and respectful of a candidate's time. Talent Tick gives candidates a secure link, open for around five hours, to answer their 10 questions whenever suits them. The risk people raise is obvious: nobody is watching. But the behavioural signals above turn "nobody is watching" into "the process notices what matters" — without recording someone's living room.

The point of the signals is to inform a live conversation. If the async answers look strong but the time-and-paste data is odd, you bring a couple of those answers into a short live call and ask the person to explain their reasoning. Real understanding survives that. Pasted AI output usually does not.

What not to do

Resist the surveillance arms race. Lockdown browsers and webcam proctoring frustrate honest candidates, raise privacy and accessibility issues, and still get beaten by a second device. Worse, they signal distrust before someone has even joined.

• Do not accuse based on a single signal.
• Do not treat AI-assisted polish as cheating — using AI to draft is normal now.
• Do reserve scrutiny for the combination of weak reasoning under live questioning plus suspicious behavioural data.

Honest assessment is a design problem more than a policing problem. Ask questions only this candidate can answer, watch the behavioural signals that genuinely matter, and confirm understanding in a short live conversation. Talent Tick's personalised questions and async self-interviews with tab, paste, and timing tracking are built to do exactly that. Start a free 21-day trial and run an honest assessment this week.