You cannot reliably prove that a remote interview answer was generated by AI from how it sounds. A polished, generic, or scripted response is a reason to ask a fair follow-up—not to accuse a candidate. Check whether the person can explain their reasoning, apply it to a new work-related situation, and substantiate claims through consistent assessment and references.
Can you tell if a candidate is using ChatGPT in a job interview?
Not with certainty from a single answer or visible behavior. AI can help a candidate prepare or phrase a response, and a polished answer alone does not establish how it was produced. Conversely, an awkward or inconsistent answer does not establish that AI was used.
Canadian Public Service Commission guidance lists possible signs—not proof—including a marked difference in style or quality between answers, generic or irrelevant responses, fake citations, experience claims that conflict with work history, and a third-person account of personal experience. In prerecorded video, apparent script reading may also raise a question. These are prompts for verification, not a reliable diagnostic checklist. Public Service Commission of Canada guidance
Do not treat pauses, accent, eye movements, nervousness, polished vocabulary, or a change in speaking style as proof. Those observations can have many explanations, and the cited guidance does not establish them as dependable indicators of AI use.
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Why polished answers and AI detectors are weak evidence
AI assistance can improve an answer without proving misconduct
A preregistered experiment by Canagasuriam and colleagues assigned 245 participants to complete asynchronous video interviews using no ChatGPT, verbatim ChatGPT responses, or personalized ChatGPT responses. The ChatGPT groups received higher overall and content scores than the control group, while delivery scores did not differ; they also received lower honesty ratings. This is evidence from one experimental prerecorded-interview setup, not a validated way to identify AI use in a live remote interview. Canagasuriam et al., 2025
Text detectors are not reliable arbiters of spoken answers
The Canadian guidance cautions that detector reliability may be questionable and that false positives can seriously affect candidates. The asynchronous-video study also notes that text detectors were designed for writing rather than spoken video answers, and that classifying transcripts accurately is uncertain. Do not make an adverse decision based only on a detector score or automated accusation.
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What to ask when an answer sounds AI-generated
Use a neutral, job-related follow-up that tests understanding and ownership. Ask the same core questions and apply the same scoring criteria to candidates in comparable circumstances. Avoid trick questions; the aim is to learn whether the candidate can explain the work and reasoning behind the answer.
- Clarify personal contribution: “What part of that work did you personally own?”
- Probe a decision: “What trade-off led you to that choice?”
- Ask for the reasoning: “Can you walk me through how you reached that result?”
- Change a relevant constraint: “If the deadline were cut in half, what would you change and why?”
- Check a claim: “Which outcome can you attribute to your work, and how was it measured?”
A candidate who can discuss decisions, constraints, and their own role offers more useful evidence than an answer’s polish alone. A weak follow-up is not proof of AI use either; assess the response against the role’s requirements and the same rubric used for other candidates.
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How to verify the candidate’s skills fairly
Compare the answer with other evidence
Check whether examples and qualifications align with the candidate’s stated work history and the rest of the interview. Use structured questions, a suitable job-related assessment, and reference questions where appropriate. Canadian federal guidance recommends interviews, scenarios, and references as ways to confirm that qualifications belong to the candidate rather than relying on AI-generated information. Its recommendations are guidance for the federal public-service context, not universal law.
Record specific evidence, not a suspicion
Document the answer or inconsistency that prompted a follow-up, the question asked, and the candidate’s response. Keep notes tied to the published criteria—for example, whether the candidate could explain a claimed result—not impressions such as “sounds like ChatGPT.” If you believe a stated rule may have been breached, use the process announced to candidates and the applicable jurisdictional policy. Do not substitute a detector result for that process.
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Set clear AI rules before the interview
Decide what the assessment is meant to measure. If responsible AI use is part of the job, assess that skill directly. If the stage is intended to measure unaided performance, state the restriction and provide a consistent way to enforce it. Canadian guidance advises employers to clarify whether AI is allowed, which tools and stages are covered, whether disclosure is required, and what happens if rules are breached. It also flags fairness issues involving paid tool access, language, technical knowledge, and computer equipment.
| Policy choice | When it may fit | What to specify |
|---|---|---|
| Permit defined AI use | When AI use reflects the actual work or the assessment is intended to evaluate AI-assisted practice. | Allowed tools and stages; whether candidates must disclose use; how you will verify the candidate’s own judgment and qualifications; and safeguards for protected information. |
| Prohibit AI for defined stages | When the assessment is intended to measure unaided skills that are relevant to the role. | Which stages and tools are prohibited; how the rule will be communicated and applied consistently; and the announced response to a suspected breach. |
Whichever policy you choose, communicate it before candidates are assessed and apply it consistently. Consider whether candidates have equitable access to the tools, equipment, and language support the process assumes.
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Evidence from other selection settings can inform assessment design, but it does not establish how often candidates use AI in live remote job interviews or provide a universal detection rate.
- Virtual medical-school admissions: A 2025 randomized virtual multiple mini-interview (MMI) study analyzed 140 participants. In a modified format that removed isolated preparation time and had an examiner read the prompt, the ChatGPT group performed worse than both control groups. In the standard timing condition, there was no statistically significant difference. The modified format appeared to reduce rating consistency, so it is a tested procedural trade-off in medical-school admissions—not a general recommendation for hiring interviews. Virtual MMI study, 2025
- U.S. pre-hire assessments: Robie and colleagues studied an operational assessment in Q3 2024 (5,675 applicants): fewer than 3% reported using generative AI alone, while up to 19% reported using it with algorithmic resources such as search engines. In a separate Q3 2025 study of 3,356 applicants, more than 23% of control participants and 10.6% of participants shown a consequences-based warning reported at least some generative AI use. These were self-reports in pre-hire assessments; the authors reported that the warning reduced reported use. They are not prevalence estimates for remote interviews. Robie et al., 2026
For broader guidance on AI systems used in recruitment—not specifically candidates using AI to compose answers—the UK Department for Science, Innovation and Technology published a Responsible AI in Recruitment guide on 25 March 2024.
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