There is no universal rule on AI in coding interviews: the instructions for the specific assessment decide whether you may use an assistant, autocomplete, search, or other tools. Both AI-enabled and no-AI exercises can assess useful engineering skills. The difference is what the task is designed to reveal and how the employer verifies that the work reflects your own understanding.
What employers assess in a coding interview
A coding interview is not simply a test of how quickly someone can type syntax from memory. Public guidance from Microsoft Careers describes technical interviews as focused on problem-solving and role-related skills. Microsoft’s engineering interview guidance also calls out problem solving, design, coding, and testing. OpenAI’s interview guide identifies well-designed solutions, high-quality code, optimal performance, test coverage, communication, and collaboration as engineering interview signals.
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Those criteria point to a broader evaluation: how you turn an ambiguous request into a workable plan, select an approach, implement it, check whether it behaves correctly, and explain your decisions. An interviewer may care about the final output, but the process can show whether you understand why that output is appropriate and where it might fail.
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Interview prompts may leave details unspecified on purpose. Microsoft advises candidates to clarify ambiguities and make a plan. Asking whether inputs can be empty, whether duplicates are possible, or what scale to expect can change the design and prevent a candidate from solving the wrong problem. The useful signal is not asking questions for their own sake; it is identifying assumptions that affect correctness, performance, or scope.
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Design also includes choosing a suitable data structure or algorithm and recognizing trade-offs. Microsoft lists algorithms, data structures, and system design among preparation areas. A candidate should be able to explain why an approach fits the stated requirements and what it costs in complexity or maintainability. A theoretically optimal solution is not automatically the best fit if it is hard to implement correctly or exceeds what the task needs.
Implementation, testing, and explanation
Microsoft’s engineering guidance recommends writing clean, concise code, running or compiling it, testing the solution, considering security implications and boundary conditions, and explaining corner cases. OpenAI likewise names code quality, performance, and test coverage. Together, these examples make clear why “from scratch” should not be mistaken for “type everything without thinking.” Implementation is only one part of the evidence.
Testing can show whether you can turn your reasoning into checks that challenge the solution rather than merely repeat the happy path. Explanation lets an interviewer ask why a choice was made, what assumptions remain, and how the code should change if a requirement shifts. These are stated criteria in the cited employer guidance; they should not be treated as a universal scorecard used identically by every company.
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An AI-enabled assessment can resemble a work setting in which an engineer has access to tools. The Public Service Commission of Canada says allowing AI can simulate a realistic work environment, reflect professional context, and let employers assess candidates’ ability to use the tools. In that format, the relevant question is not only whether the code works, but how the candidate directs, evaluates, and integrates assistance.
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A working answer may be weaker evidence of an individual’s ability if an AI assistant generated much of it and the candidate cannot explain or verify it. That is a design implication, not a rule that every employer follows. Canadian public-service guidance recommends follow-up interviews, scenarios, or questions to confirm that the result reflects the candidate’s qualifications rather than AI-generated content. In combination with the technical criteria above, this suggests ways an employer can make the candidate’s own contribution more visible.
Signals an AI-enabled task can reveal
- Task framing: Can the candidate give the tool useful context, define constraints, and notice when the prompt or generated approach misses a requirement?
- Verification: Does the candidate inspect the output, run or compile it where appropriate, and test boundary conditions instead of accepting plausible-looking code?
- Judgment: Can the candidate identify an error, security concern, performance issue, or unnecessary complication and decide whether to keep, revise, or reject the suggestion?
- Ownership: Can the candidate explain the solution in their own words and make a change when the interviewer introduces a new constraint?
- Communication: Does the candidate make tool use and unresolved assumptions clear, rather than presenting generated output as self-explanatory?
These are possible assessment signals, not a claim that every AI-enabled interview measures all of them. The employer needs to design the task and follow-up around the role. If the job involves daily use of coding assistants, tool-assisted workflow may be relevant. If the assessment is intended to establish independent fundamentals, unrestricted assistance could obscure the very skill being measured.
How the two assessment formats differ
Neither format is inherently a better measure of engineering ability. A no-AI task can make independent reasoning easier to observe, while an explicitly AI-enabled task can examine tool use and verification in context. The meaning of a result depends on the task, the permitted tools, and whether the interviewer can establish what the candidate understood and contributed.
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|---|---|---|
| Tool rules | AI is excluded for the task if the instructions say so. Other permissions, such as documentation or autocomplete, still need to be specified. | AI use is permitted only to the extent stated in the assessment instructions; permitted tools and stages may vary. |
| Primary opportunity | Observe independent problem solving, design, implementation, testing, and explanation, consistent with criteria named by Microsoft Careers and OpenAI. | Observe tool use alongside the same engineering fundamentals, with potential attention to prompt/context quality, output evaluation, and workflow. |
| How understanding can be checked | Ask the candidate to explain choices, test cases, corner cases, and trade-offs while working. | Ask the candidate to explain the result, test and adapt it, identify errors, or respond to a follow-up requirement; Canadian public-service guidance recommends follow-up checks. |
| Access and fairness | A controlled format can limit differences in access to paid AI tools, but the employer should still clarify available equipment and permitted resources. | The employer should consider whether candidates have comparable access to the permitted tool and relevant equipment; Canadian guidance specifically flags unequal access to paid tools, languages, technical knowledge, and equipment. |
| Rule clarity | Instructions need to state what is prohibited and what remains available. | Instructions need to identify allowed tools, stages, and any disclosure expectations; employer guidance does not establish one shared policy. |
The table describes design possibilities, not guaranteed features of every interview. For example, allowing AI does not by itself prove a company is testing AI fluency, and banning it does not mean the employer only values memorized syntax. The employer’s stated purpose and the assessment details matter.
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Do employers allow ChatGPT, Copilot, or autocomplete?
Sometimes, but candidates should not infer permission from an employer’s general use of AI or from a tool being available on their own computer. OpenAI says expectations for AI and other tools vary by interview and advises candidates to consult preparation materials or ask the recruiter. ServiceNow says candidates may use AI to prepare, but evaluation stages, including technical challenges, should be completed independently unless the company explicitly permits a particular tool or use case. Disney says not to use AI to complete coding exercises or other assessments unless instructed otherwise. Deloitte distinguishes preparation from live evaluation: it permits AI for researching the organization, refining application materials, and practicing, while disallowing AI-generated interview work product and real-time assistance unless explicitly permitted.
These examples differ, but they lead to the same practical rule: the specific assessment instructions take precedence. Permission to practice with AI is not permission to use it during a live coding test. Likewise, permission for one assistant or one stage does not automatically cover autocomplete, web search, a second assistant, or another stage. If the rules do not say whether a specific tool or use case is allowed, ask the recruiter before beginning.
Preparation and evaluation are separate
Employers may welcome AI as a study aid while expecting independent work during the assessment. ServiceNow and Deloitte explicitly distinguish preparation from evaluation in their guidance; Disney and ServiceNow say assessment use depends on explicit permission. Treat practice exercises, take-home assignments, and live interviews as separate contexts unless the employer tells you otherwise.
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Do not rely on a broad statement about an organization’s approach to AI to settle a narrow question about your test. A candidate invitation may impose rules that differ by role or stage. The safest reading is the most specific one: follow the assessment’s instructions, and request clarification before you submit work if anything remains unclear.
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What the available statistics 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. The reported shares add up to 100%, but they describe that survey’s respondents, not every employer or every kind of coding interview. They do show why candidates should not assume there is a settled universal policy.
A 2024 study by Chen, Huo, Nam, Port, and Peruma surveyed 32 industry professionals. It reports mixed views on candidate AI use and says the majority of organizations represented had not adjusted candidate evaluation methods to account for AI use or knowledge. Because the survey is small, it should not be read as a census of employers. The cited evidence does not establish a single prevalence figure for all employers or prove that one interview format predicts job performance better across roles.
Accordingly, claims that AI-enabled coding interviews are now standard, that traditional coding interviews are obsolete, or that employers universally prioritize AI fluency over fundamentals go beyond what these findings establish. The more defensible conclusion is that policies and practices vary, while published employer guidance continues to name core engineering skills.
How candidates should handle an assessment
Before starting, resolve the tool rules rather than guessing. Then focus on making your reasoning and work verifiable, whether the assessment permits AI or not.
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- Read the invitation and assessment instructions. Check for rules about AI, autocomplete, search, documentation, collaboration, and which stages the rules cover.
- Ask about anything ambiguous before the assessment. Name the tool or use case you want to clarify, such as an AI assistant, IDE autocomplete, or web search. Do not assume a general permission covers all tools.
- Follow any disclosure requirement. If the instructions say to report tool use, do so accurately. If they do not address disclosure but permit AI, ask how the employer expects use to be handled.
- Clarify the task before committing to an approach. Identify consequential assumptions, describe a plan, and state trade-offs so the interviewer can follow your reasoning.
- Check the result. Run or compile the solution when possible, test typical and boundary cases, and examine failures rather than relying on output that merely looks plausible.
- Be prepared to explain and adapt. Explain the approach, important decisions, and limitations. If asked to change a requirement, work through the implications rather than treating the submitted answer as finished and untouchable.
For preparation, practice both modes if the target employer’s policy is unknown: solve problems without AI to strengthen independent reasoning, then use permitted tools in separate practice to learn how to evaluate assistance. Microsoft identifies algorithms, data structures, and system design as preparation areas; OpenAI advises candidates to check interview-specific materials for tool expectations. Practicing both ways does not imply that both will be allowed in the actual assessment.
How employers can make an assessment fair and informative
For employers, the choice is not simply “AI or no AI.” A useful assessment should match the role and make the candidate’s relevant contribution visible. A controlled no-AI round can be suitable when independent fundamentals are the target. An explicitly AI-enabled exercise can be suitable when tool-assisted workflow is job-relevant, provided the exercise also checks whether the candidate can assess and explain the result. These are design options, not evidence that every organization uses either approach.
Set expectations before the task
Canadian public-service guidance recommends stating which tools are allowed and at which stages, communicating consequences, and considering unequal access to paid tools, languages, technical knowledge, and equipment. Clear instructions should distinguish preparation from evaluation and specify whether candidates may use an assistant, autocomplete, search, or other resources. If the employer permits a particular tool, it should consider whether candidates have comparable access rather than assuming everyone does.
Make the candidate’s contribution observable
Assess whether the task can be answered by AI in a way that obscures the candidate’s qualifications, then adjust the design if necessary. The Canadian guidance recommends that employers consider this risk and use additional interviews, scenarios, or follow-up questions to confirm qualifications. A discussion of the submitted solution, a request to explain a corner case, or a changed requirement can help reveal whether the candidate understands the work. These approaches are examples consistent with the guidance, not a fixed required script.
Align the evaluation with the job
If a role requires developers to work with AI tools, an assessment that allows those tools may offer relevant evidence of tool use in context. If the hiring question is whether someone can reason through a fundamental problem independently, an explicitly controlled task may fit better. In either case, employers can state what they intend to measure and assess the related skills—problem solving, design, implementation, testing, performance, communication, or collaboration—rather than treating tool use alone as a proxy for engineering ability.
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