How to Assess Coding Skills When Candidates Use AI

When every candidate can open Copilot or ChatGPT mid-interview, the real question is not whether they used AI. It is whether the candidate can explain, defend and extend what the AI produced, in front of you, under a follow-up question. Getting a reliable signal from an AI-proof coding interview questions approach does not come from detection alone: redesign the question so a generated answer cannot pass unexplained, add live follow-up questions that test reasoning, decide your AI policy before the interview starts, and score every candidate against the same criteria so the result holds up to scrutiny.

Key takeaways

• Classic algorithm prompts, reversing a linked list, solving two-sum, are now solvable by almost any candidate with AI assistance before they finish reading the question, so they measure tool access rather than coding skill.
• Question design and interview monitoring are two separate layers: design decides whether a generated answer can pass at all, monitoring decides whose reasoning produced it.
• A live follow-up question, asking the candidate to explain a specific line or defend a trade-off out loud, is usually enough to separate genuine understanding from a pasted answer.
• Decide an explicit AI policy (AI-native, conditional or banned) before you write the question, because the policy changes what "passing" actually means.
• Score every candidate against identical criteria tied to evidence in the recording, so AI use does not quietly become the deciding factor and the hiring decision can be defended later.

Why Have Standard Coding Interview Questions Stopped Measuring Skill?

Classic algorithm-style prompts stopped measuring coding skill the moment every candidate had an AI copilot open in a second window. A question like "reverse a linked list" or "find two numbers that sum to a target" can be solved correctly by pasting it into an AI tool before the candidate has finished reading it out loud. Resume Genius's 2026 Job Seeker Insights Report, covered by Newsweek, found that 22% of job seekers already use AI during real-time interviews, and that is only self-reported use candidates were willing to admit to.

Memorized patterns plus AI assistance mean the question is measuring whether a candidate has tool access, not whether they can reason about code. That is not a minor scoring problem. It is a hiring problem: a recruiter cannot defend a shortlist decision to a hiring manager, and a hiring manager cannot defend an offer to finance or leadership, if nobody can say with confidence who actually produced the answer.

This sits inside a broader shift in technical and coding interviews generally, not just this one question type: hiring teams are rethinking how they evaluate developers now that AI assistance is a normal part of a candidate's toolkit, not an edge case to police.

Separate the Question From the Monitoring: They Solve Different Problems

Question design and interview monitoring solve two different problems, and treating them as one choice between detection and permissiveness is a false frame. A question can be well designed and still get gamed if nobody is watching how the answer was produced; a session can be perfectly monitored and still produce a meaningless result if the question itself can be solved by pasting it into a chatbot.

Question design decides whether a generated answer can pass at all. Monitoring decides whose reasoning produced it.

A defensible hiring decision is one you could explain to the candidate, and still stand behind if a hiring manager or an auditor challenged it. That standard needs both layers working together, not one standing in for the other.

This same logic underpins the broader practice of designing AI interview questions candidates cannot script their way through: decide what a generated answer can and cannot pass first, then decide how you will confirm whose work it is. Zinterview runs its coding interview editor, adaptive follow-up questioning and proctoring inside one interview flow, so a hiring team is not stitching together a separate code editor, a separate monitoring tool and a separate scoring process by hand.

What Does an AI-Proof Coding Interview Question Look Like?

No coding question is completely immune to AI assistance. The realistic goal is a question that still produces a defensible signal when AI is in the room, not a question AI can never touch. The practical version of an AI-proof coding interview question replaces a closed algorithm puzzle with an ambiguous, real-world problem that needs a product or systems judgment call, requires the candidate to state and defend assumptions before writing a line of code, and builds in a wrinkle that only becomes visible once requirements are clarified.

An AI-Proof Coding Interview Question in Practice

A vague "build this" prompt with a hidden constraint, revealed only once the candidate asks a clarifying question, does the real work here. A generic AI-generated answer tends to solve the stated problem cleanly and miss the constraint entirely, because the constraint was never in the prompt it saw. A candidate who asks the right clarifying question before coding has already shown a skill no copilot can supply on its own: knowing what to ask.

Use Live Follow-Up Questions to Separate Real Understanding From a Pasted Answer

A live follow-up question, asked out loud during the interview, is the fastest way to tell genuine understanding from a pasted answer. Ask the candidate to explain a specific line of their own code, or defend a trade-off they chose over an alternative. Then introduce a small twist partway through, a changed requirement or an edge case, and watch how the reasoning adapts.

A generated answer that looked clean on arrival tends to break on the first follow-up question.

This is where adaptive questioning earns its place over a fixed script. Zinterview's AI Mode generates follow-up questions that adapt to what the candidate just said, and asks for clarification automatically when an answer needs it, rather than relying on a human interviewer to improvise a good follow-up on the spot for every candidate.

Decide Your AI Policy Before You Write an AI-Proof Coding Interview Question

Three postures show up across hiring teams right now, and each one changes what "passing" means for the exact same question.

Canva's engineering team has gone furthest toward the first posture: it now expects backend and frontend engineering candidates to use tools like Copilot, Cursor or Claude during a live coding round, and evaluates how well they prompt, review and debug the AI's output rather than whether they used it at all.

Whichever posture fits your hiring process, decide it before you write the question, not after a candidate's result is already confusing to score. The posture maps directly onto how you configure the interview: Zinterview's Standard mode runs the identical question set and weighting for every candidate, which suits a conditional or banned posture where comparability matters most, while AI Mode's adaptive questions suit an AI-native posture for senior or specialized roles where the judgment being tested is how well someone directs a tool, not whether they touched one.

Score for a Decision You Can Defend, Not Just a Working Solution

Scoring for a defensible decision means evaluating whether the candidate can justify their design choices and catch a mistake you deliberately planted, not just whether the code runs. Keep the scoring criteria identical across every candidate for the role so that AI use does not quietly become the deciding factor between two people who were never evaluated on the same terms.

Evidence matters here as much as the score itself. Zinterview links every question score to the specific moment in the interview recording it came from, so a recruiter reviewing a flagged or borderline result has an actual exchange to look at, not a single number with no context. That evidence also keeps a human in the loop by design: a flag from monitoring is something a recruiter reviews and decides on, never an automatic rejection.

That human-in-the-loop design matters for another reason: recruiters and candidates do not currently agree on what fair AI-assisted hiring looks like, a gap documented in the AI hiring trust gap between recruiters and candidates. A scoring process that is identical for every candidate, and that a recruiter can actually see the evidence behind, is one of the more concrete ways to close that gap rather than widen it.

See how Zinterview combines adaptive follow-up questioning with proctoring in one coding interview flow. Start a free trial.

Frequently Asked Questions

Is it OK to use Copilot in a coding interview?It depends entirely on the policy the hiring team sets, and that policy should be decided before the interview, not discovered mid-session. Some teams, like Canva's engineering org, now expect candidates to use tools like Copilot openly and evaluate how well they direct it. Others allow it only if the candidate can explain every line, and others ban it outright and proctor to enforce that. There is no universally correct answer, only a policy that matches what the question is designed to test.

How do you stop candidates from cheating with AI in coding interviews?Treat it as two separate problems. Redesign the question so a generated answer cannot pass without the candidate clarifying requirements and defending assumptions out loud. Then add monitoring, identity verification, screen lockdown and a second camera angle, to confirm the person being evaluated is the one actually producing the reasoning. Neither layer alone is enough; a well-designed question still needs monitoring, and monitoring alone does not fix a question AI can solve outright.

Should companies ban AI tools in technical interviews?Not necessarily. A ban is one valid posture, and it suits roles where closed-book problem solving is genuinely what the job requires. But an increasing number of engineering teams now treat AI fluency itself as part of the job and design interviews around it instead. The posture should follow from what the role actually needs on day one, not from discomfort with the tool.

What makes a coding interview question AI-proof?No coding interview question is completely AI-proof, and treating "AI-proof" as an absolute is itself a mistake. What makes a question hold up is an ambiguous, real-world scenario that requires a judgment call AI cannot make on the candidate's behalf, a requirement to state and defend assumptions before coding, a hidden constraint that only surfaces through clarifying questions, and a live follow-up that tests whether the candidate understands what they produced.