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Because an interview is supposed to measure specific qualifications—and sometimes those are unaided problem-solving skills, not AI-assisted output. But if using AI is part of the job, employers can reasonably test that too. The sound policy is not “AI is always cheating” or “AI should always be allowed”; it is to match the rules to the skill being assessed and tell candidates what those rules are.
Why allow AI at work but restrict it in an interview?
The two settings can measure different things. An employer may expect engineers to use AI tools in their day-to-day work while using a particular interview exercise to assess unaided coding knowledge, reasoning, or communication. If AI generates the solution during that exercise, the employer may no longer be measuring the intended skill.
That does not make AI use inherently dishonest. It makes the assessment’s purpose decisive. The Public Service Commission of Canada says, “The use of AI could be relevant if access to these tools is part of the job.” Its guidance applies to Canadian federal public-service appointments, not as a universal rule for employers elsewhere. The Commission’s guidance recommends that employers align assessment strategy with the qualifications being evaluated and state rules and consequences clearly.
Can you use ChatGPT or Copilot during a coding interview?
Only if the employer’s instructions for that stage permit it. Policies differ, and permission to use AI to prepare does not necessarily mean permission to use it for live answers or coding solutions.
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Examples of how employers draw the line
- Cisco: Its published guidance permits GenAI for preparation, clarification, brainstorming, and code review. It identifies “Generating the solution to a coding problem or design question in real time” and reading AI-generated interview scripts as inappropriate uses during its hiring process. Cisco’s interview guidance is an example of that company’s policy, not a general hiring standard.
- Bumble: Its guidance encourages AI for research, practice, and refining preparation, but discourages generating live interview responses unless the stage is explicitly an AI collaboration assessment. Bumble says some software engineering interviews include such an exercise and that take-home briefs specify AI’s role. The page was last updated July 24, 2026. Read Bumble’s candidate guidance.
When instructions are unclear, ask the recruiter or interviewer before the assessment begins. Clarify whether AI is allowed for brainstorming, code completion, debugging, documentation, or only specified tasks; which tools are permitted; and whether you must disclose your use. Do not assume a tool is allowed just because it is available on your device.
Are companies testing how engineers work with AI?
Some are. An AI-enabled exercise can let an employer observe whether a candidate can frame a task for a tool, evaluate its output, catch errors, and adapt the result. That can be relevant when the job itself requires those capabilities. Bumble’s published policy, for example, says it will tell candidates in advance if a stage is designed to assess how they work with AI.
But an AI-enabled interview does not isolate engineering skill automatically. Results can also depend on familiarity with a particular tool, prompting experience, access to a stronger model, and judgment in checking generated output. An assessment may therefore measure a mix of engineering ability and AI fluency—intentionally or not.
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Employers need to decide what each exercise is meant to measure before choosing whether AI is allowed. The Canadian Public Service Commission recommends stating permitted tools, the stages where they may be used, and consequences for breaking the rules. It also suggests follow-up interviews or scenarios to confirm qualifications. Its advice cautions that AI-detection tools may be unreliable and that false positives can harm candidates.
For an AI-enabled exercise, assessment-design researchers Dunlop and coauthors identify validity and fairness concerns, alongside reliability, security, and implementation challenges. Employers can reduce ambiguity by checking these design choices:
- Target skill: Is the exercise testing unaided fundamentals, AI literacy, or both? If it measures both, does the scoring distinguish them?
- Job realism: Does the task reflect work engineers actually do in this role, including any expected use of AI?
- Equal conditions: Do candidates have the same approved tools and comparable access?
- Scoring and transparency: Can the employer explain what is assessed and how tool use affects the result?
- Reliability and security: Can the exercise produce comparable results without exposing confidential assessment material?
- Candidate clarity: Are the stage, rules, allowed tools, disclosure expectations, and consequences communicated beforehand?
What does the available evidence say—and not say?
A 2026 study by Robie, Wingate, Baytalskaya, and Butera examined applicants who took part in a Q3 2024 assessment study. Among 5,675 applicants, fewer than 3% self-reported using generative AI, while up to 19% reported using it together with algorithmic resources such as search engines. These figures describe that study’s self-reports, not engineering candidates generally.
A second study included 3,356 applicants and was conducted in Q3 2025. It examined a consequences-based warning; the authors report that self-reported GenAI use rose from 2024 to 2025 and that warnings reduced reported use. These studies document reported use and the effect of assessment conditions. They do not establish that AI-enabled engineering interviews predict job performance better than interviews that prohibit AI, nor do they show that one format is superior across roles. The authors’ study is not an engineering-only sample.
The employer policies above likewise show that rules vary; they do not establish how common any policy is across employers. The practical conclusion is narrower: restrictions can serve a legitimate measurement purpose, and AI collaboration can also be a legitimate assessment target when it matters to the job. Candidates need clear, stage-specific instructions to know which one applies.
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