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There is no universal rule on AI in coding interviews. Some assessments prohibit assistants and autocomplete; others permit them to model day-to-day engineering. In either format, employers still look for evidence that you can understand a problem, choose a sound design, produce correct and maintainable code, test it, and explain your decisions. The difference is whether the assessment also measures how well you use, verify, and communicate with development tools.
The invitation and assessment instructions for your specific interview control. Microsoft, OpenAI, ServiceNow, Disney, and Deloitte all describe different boundaries, while a 2026 CoderPad survey found no consensus among respondents. Treat AI permission as an assessment rule to confirm—not an assumption based on the company’s general use of AI.
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What employers assess when you write code without AI
A no-AI exercise gives an interviewer a direct view of your independent reasoning. Microsoft Careers describes technical interviews as focused on “problem-solving and the skills needed for the role.” Its engineering guidance identifies a lifecycle that includes problem solving, design, coding, and testing. OpenAI’s interview guide similarly emphasizes well-designed solutions, high-quality code, optimal performance, test coverage, communication, and collaboration.
Those criteria mean “from scratch” is not simply a memory test for syntax. A candidate can forget an API name and still demonstrate strong engineering by clarifying the requirement, selecting an appropriate approach, and checking the result. Conversely, code that compiles but rests on an unexplained or unsuitable design exposes a weakness even when every line was typed unaided.
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Problem framing and clarification
Interviewers watch whether you identify missing information before implementing. Ask about input limits, expected output, error behavior, data quality, concurrency, and security assumptions. Restate the goal in your own words and name the constraints you will use to judge a solution. This makes your interpretation visible and prevents a polished answer to the wrong problem.
Design and trade-offs
Explain the data structures, interfaces, and decomposition you plan to use. Discuss time and space complexity when they matter, but connect those figures to the stated constraints rather than reciting Big-O notation. If you choose a simpler approach over a theoretically faster one, say why: the input may be small, the simpler code may be easier to verify, or operational limits may dominate.
Implementation quality
Employers commonly assess whether code is readable, concise without being cryptic, consistent with the language, and organized so another engineer could maintain it. Name variables by purpose, isolate responsibilities, handle errors deliberately, and avoid copying patterns you cannot explain. In a system-design discussion, connect components and failure behavior; in a small coding task, show the same habits at a smaller scale.
Testing, boundaries, and security
Run or compile the code when the environment allows it. Test a normal case, an empty or minimal case, a maximum or unusually large case, and invalid or adversarial input. Explain what a failing test would tell you and how you would localize the defect. Microsoft’s guidance also calls out security implications and boundary conditions, so mention authentication, authorization, injection, data exposure, or resource exhaustion when the problem makes them relevant.
Communication and collaboration
Think aloud at a useful level: state the decision, the reason, and the consequence. Invite a clarification instead of silently guessing. When an interviewer challenges an assumption, respond by updating the model and identifying what changes in the implementation or tests. OpenAI’s guide treats communication and collaboration as part of the engineering signal, not as a separate courtesy score.
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Algorithms, data structures, and system design
Microsoft lists algorithms, data structures, and system design among preparation areas. The exact emphasis depends on the role. A backend position may probe indexing, queues, consistency, and failure recovery; an application role may focus on state management, API boundaries, and testability. The common signal is the ability to connect an abstract technique to the requirements in front of you.
What an AI-enabled coding task can measure
Allowing an assistant can make an assessment resemble a professional environment in which engineers search documentation, use an editor, and ask tools to generate a first draft. The Public Service Commission of Canada says employers can allow AI throughout hiring to simulate a realistic work environment, reflect professional context, and assess candidates’ ability to use the tools. That does not make the generated output the whole assessment.
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When an assistant supplies substantial code, the observable skill shifts toward judgment and verification. A well-designed exercise can ask you to explain the proposed approach, inspect for incorrect assumptions, run tests, identify edge cases, correct a subtle bug, and adapt the implementation after a requirement changes. These activities help distinguish ownership of the result from merely accepting a plausible completion. The Canadian guidance recommends additional interviews, scenarios, or follow-up questions to confirm that the result reflects the candidate’s qualifications.
Prompting and context management
An interviewer may observe whether you can give a tool precise, relevant context: the contract, constraints, existing interfaces, style requirements, and known risks. Effective use is not a contest to produce the longest prompt. It is the ability to break work into verifiable pieces, reject an answer that conflicts with the codebase, and request a focused revision.
Verification and testing
Generated code can contain wrong assumptions, insecure defaults, hidden performance costs, or APIs that do not exist in the specified version. Read it line by line. Compile or run it, add tests that target likely failure modes, and compare behavior with the requirement. If a tool proposes a library, confirm that the dependency and version are permitted rather than treating the suggestion as authoritative.
Explanation and adaptation
Be ready to explain every important line, data flow, and trade-off without asking the assistant to speak for you. A follow-up change—such as a new input constraint, a failure requirement, or a request for a different interface—tests whether you can modify the solution rather than restart blindly. This is an assessment-design implication drawn from the verification recommendations and from employer criteria for communication, testing, and problem solving; it is not a universal rule that every company uses the same follow-up.
AI-assisted coding and scratch coding compared
| Assessment dimension | Writing without AI | AI-enabled task |
|---|---|---|
| Tool permission | AI assistants, external search, or autocomplete may be prohibited; the written instructions decide. | An assistant or specified tools may be allowed, sometimes with limits on models, accounts, or features. |
| Primary signal | Independent problem framing, design, implementation, testing, and explanation. | Tool-assisted workflow plus the ability to direct, inspect, verify, and improve generated work. |
| How understanding is visible | Reasoning while solving, tests you devise, and your explanation of the finished code. | Prompt context, review of output, tests, disclosure, explanation, and responses to changed requirements. |
| Access and fairness | A controlled environment can reduce tool differences, although candidates may still vary in familiarity with the language and editor. | Employers should consider unequal access to paid tools, languages, technical knowledge, and equipment; employer-supplied access is not universal. |
| Rule clarity | The invitation should identify prohibited assistance and any allowed reference material. | The invitation should name the permitted assistant and limits; silence should not be interpreted as permission. |
Neither format is automatically a better predictor of job performance across all roles. A no-AI round can reveal fundamentals that a tool might mask. An explicitly AI-enabled task can reveal whether you can work responsibly with tools you will encounter on the job. The value depends on matching the exercise to the role and making the candidate’s own contribution observable.
Do employers allow ChatGPT, Copilot, autocomplete, or search?
Sometimes, but only when the assessment rules say so. OpenAI’s interview guide states: “Expectations for AI and other tools vary by interview.” It advises candidates to check the preparation materials or ask the recruiter. ServiceNow’s candidate guidance says, “We will be clear when AI use is permitted.”
Policies can differ between preparation and evaluation:
| Employer guidance | What it means for a candidate |
|---|---|
| ServiceNow | AI may be used to prepare, but evaluation stages, including technical challenges, should be completed independently unless the company explicitly permits a tool or use case. |
| Disney | Do not use AI to complete coding exercises or other assessments unless instructed otherwise; permitted use will be communicated. |
| Deloitte | AI is permitted for researching the organization, refining application materials, and practicing, but AI-generated interview work product and real-time assistance are disallowed unless explicitly permitted. |
| OpenAI | Expectations vary by interview; consult the preparation materials or ask the recruiter. |
These examples answer common questions directly: you cannot infer permission from a company’s public enthusiasm for AI, from tools employees use internally, or from the fact that an editor offers autocomplete. If the rules do not specifically address ChatGPT, Copilot, an IDE completion feature, a search engine, documentation, or another person’s help, ask before starting and keep the answer in writing.
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A candidate checklist before and during the assessment
- Read the complete invitation. Look for a section on external resources, AI, autocomplete, recording, collaboration, and disclosure. Note whether the rule applies to the take-home exercise, live interview, screen-share session, or all stages.
- Resolve ambiguity with the recruiter. Ask a narrow question that names the exact tool and action: for example, whether language-server completions are allowed, whether documentation search is allowed, or whether a named assistant may be used only for syntax lookup. Do not substitute a general “Is AI okay?” for the specific behavior you intend.
- Record the boundary. Save the written response and follow any requirement to disclose prompts, generated sections, or tool names. If the employer provides a controlled environment, use that environment rather than a personal account.
- Start with your own plan. Write the requirements, assumptions, approach, and test cases before requesting assistance. In an AI-enabled task, this gives you a baseline against which to judge the output.
- Inspect every suggestion. Check types, error handling, complexity, dependency versions, security, and consistency with the existing code. Reject code you cannot explain.
- Run meaningful tests. Include ordinary, boundary, invalid, and adversarial cases. Report what passed, what failed, and what you changed. A green result from one happy-path test is not proof of correctness.
- Prepare to explain and adapt. Be able to walk through the design without the tool open. Practice changing a requirement and predicting which code and tests must change.
- Disclose honestly. If the instructions require disclosure, list the tool and the way you used it. Concealing prohibited assistance can invalidate an otherwise strong technical result.
Should you practice coding without AI?
Yes. Independent practice remains useful even when a target role allows assistants. It builds the mental models needed to evaluate generated code and prevents a tool from becoming a substitute for understanding. Practice should have two tracks rather than treating one format as universally correct.
| Practice track | Exercises | Skill it develops |
|---|---|---|
| Independent fundamentals | Solve a problem in a blank editor; state assumptions; derive a design; implement; test; and explain complexity without an assistant. | Problem framing, algorithms, data structures, debugging, and communication under time pressure. |
| Tool-assisted workflow | Ask an assistant for alternatives, review the output for defects, write adversarial tests, measure behavior, and revise after a requirement change. | Prompting, code review, verification, safe integration, and productive collaboration with tools. |
Use the role and the published rules to set the mix. A company that prohibits assistance in live evaluation requires more independent timed practice. A role that explicitly evaluates an AI-enabled workflow requires you to demonstrate disciplined tool use, not merely faster generation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How employers can design a fair and useful assessment
Match the format to the job
Use a controlled no-AI exercise when the role requires independent fundamentals that tools could obscure. Use an AI-enabled task when responsible tool use is genuinely part of the work. Do not add an assistant solely to signal that an organization is modern; the exercise should produce evidence relevant to the position.
State the rules and consequences
The Canadian public-service guidance recommends saying which tools are allowed and at which stages, communicating consequences, and explaining how use will be handled. Define whether autocomplete, documentation, internet search, code execution, model-generated text, and human collaboration are permitted. Tell candidates whether they must disclose prompts or generated code and what happens if they violate the boundary.
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Ask for a short design explanation, tests, a code review, or a follow-up change. A second conversation or scenario can verify that the candidate understands the submitted work. This is especially important when a polished answer could have been generated with little candidate involvement.
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Account for unequal access
Assess whether a task can be answered by AI in a way that hides qualifications, as the Canadian guidance advises. Consider differences in paid-tool access, programming-language familiarity, technical knowledge, hardware, bandwidth, and accessibility needs. If a particular assistant is required, supplying a controlled account or environment can reduce, but does not automatically eliminate, these disparities.
Use multiple signals
Combine the coding artifact with reasoning, tests, communication, and follow-up evidence. No single exercise establishes every dimension of engineering ability, and the available sources do not show that one format predicts job performance better for all roles.
What the available numbers do—and do not—show
CoderPad’s State of Tech Hiring 2026 reports that 34% of respondents ban AI in technical assessments, 46% allow it broadly or with constraints, and 20% decide case by case. These figures describe that survey’s respondents; they are not a census of employers.
A 2024 paper by Chen, Huo, Nam, Port, and Peruma surveyed 32 industry professionals. It reports mixed opinions about candidate AI use, and says most organizations represented had not adjusted evaluation methods to account for AI use or knowledge. The small sample limits how far the finding can be generalized.
Together, these sources support a cautious conclusion: there is no established single prevalence rate, no evidence that AI-enabled interviews are now universal, and no basis for saying traditional coding interviews are obsolete or that every employer values AI fluency above fundamentals.
A practical decision rule
- If the instructions explicitly prohibit AI or outside help: do not use an assistant, autocomplete, search, or another person’s solution unless the rule expressly distinguishes one of them as allowed.
- If the instructions explicitly allow a named tool: stay within the stated limits, disclose use when requested, and treat generated code as a draft requiring your own testing and explanation.
- If the instructions are silent: pause and ask the recruiter. Silence is not reliable permission.
- If the employer changes the format during the process: ask which rules apply to the new stage; a take-home policy may not carry over to a live session.
The strongest candidate in either format can show a traceable chain from requirement to design, implementation, tests, and explanation. AI may shorten keystrokes, but it does not remove the need to own the decisions or the consequences.
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