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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Employers turn AI off in some engineering interviews to measure unaided coding, reasoning, or communication—not because AI use is inherently wrong. If working with AI is part of the job, an employer can instead design an assessment that allows it and evaluates how candidates prompt, verify, and adapt its output. The key is to match the interview to the skill being assessed and tell candidates the rules in advance.
Why an employer might prohibit AI in one interview
An interview is a measurement tool. A no-AI coding exercise can help an employer assess what a candidate can reason through independently: understanding a problem, choosing an approach, writing code, and explaining trade-offs. If candidates can use AI without a consistent policy, results may reflect not only those skills but also differences in tool access, familiarity, prompting ability, or willingness to use assistance.
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That does not make an AI-free exercise a complete picture of how someone will perform at work. If engineers routinely use AI in the role, an unaided test may be less like the real working environment. The employer has to decide which capability the interview is meant to measure.
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When AI belongs in the assessment
When AI collaboration is a meaningful job skill, allowing it can make the interview more representative. The employer can observe how a candidate frames a request, checks generated code, catches errors, and decides what to keep or change. The Public Service Commission of Canada says, “The use of AI could be relevant if access to these tools is part of the job.” That is guidance for Canadian federal public-service appointments, not a universal rule for private employers or other jurisdictions.
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An AI-enabled exercise also changes what the result measures. It may combine engineering fundamentals with AI literacy, tool familiarity, and judgment about generated output. Dunlop and coauthors identify validity and fairness concerns, as well as reliability, security, and implementation challenges, for AI-enabled assessments. A well-designed exercise therefore needs a clear target and comparable conditions—not simply permission to open a chatbot.
How employers can choose a fair format
Employers should define the qualifications they intend to assess and choose a format that reflects the role. The Public Service Commission of Canada advises employers to state the permitted tools and stages, explain rules and consequences, and consider follow-up interviews or scenarios to confirm qualifications. It also cautions that AI-detection tools may be unreliable, and false positives can harm candidates.
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- Target skill: Decide whether the exercise measures unaided fundamentals, AI collaboration, or both, and score those dimensions distinctly where possible.
- Job realism: Use AI when it reflects the work candidates would actually do, rather than as an unexplained obstacle or novelty.
- Equal conditions: Provide comparable tools and access if AI use is part of the test; otherwise differences in access can affect results.
- Scoring and transparency: Explain what matters—for example, the correctness of a solution, the quality of verification, or the reasoning behind revisions.
- Reliability and security: Check that the task produces comparable evidence across candidates and that the tool will not expose confidential assessment material.
- Candidate clarity: State the rule for each stage, approved tools, disclosure expectations, and consequences before the assessment begins.
What current employer policies show
There is no single rule followed by every employer. Published policies illustrate how preparation, live interviews, and AI-specific exercises can be treated differently.
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| Employer | Preparation | Live interview or assessment |
|---|---|---|
| Cisco | Allows GenAI for preparation, clarification, brainstorming, and code review. | Lists “Generating the solution to a coding problem or design question in real time” and reading AI-generated interview scripts as inappropriate uses. |
| Bumble | Encourages using AI to research, practice, and refine preparation. | Discourages AI-generated live responses unless AI collaboration is explicitly part of the assessment. Some software engineering interviews include an AI collaboration exercise; take-home briefs specify how AI fits. Policy last updated July 24, 2026. |
These are examples of the named companies’ policies, not universal hiring rules. The Canadian federal guidance likewise recommends clear, stage-specific instructions; it does not prescribe one policy for every interview.
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Can you use ChatGPT or Copilot during a coding interview?
Only if the interviewer or written instructions permit it. Permission can differ between preparation, a take-home task, and a live interview. Cisco’s policy, for example, allows some AI use for preparation but bars real-time generation of coding or design solutions. Bumble says it will tell candidates upfront when a stage is designed to assess AI collaboration.
If the instructions are unclear, ask the recruiter or interviewer before using a tool. Do not assume that permission to use AI at work—or to prepare with it—also permits it during a live assessment.
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Why allow AI at work but not in the interview?
The employer may be separating two questions: “Can you solve this unaided?” and “Can you use AI effectively as part of the job?” A traditional exercise can provide evidence for the first; a deliberately AI-enabled task can provide evidence for the second. A company that prohibits AI in every stage may miss a relevant job skill, while one that allows it without consistent rules may make candidates’ scores difficult to compare.
The available evidence does not establish that AI-enabled engineering interviews predict job performance better than no-AI interviews. A study by Robie, Wingate, Baytalskaya, and Butera included 5,675 applicants in Q3 2024: fewer than 3% self-reported GenAI use, while up to 19% reported using GenAI together with algorithmic resources such as search engines. Those figures describe self-reports in that study, not engineering candidates generally. A second study included 3,356 applicants in Q3 2025 and examined a consequences-based warning; its authors reported that self-reported use increased from 2024 to 2025 and that warnings reduced reported use. These studies document candidate behavior and policy effects, but they do not settle which interview format better predicts engineering performance.
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