Test Type AI-assisted programming

AI-assisted programming test

The candidate codes with an AI assistant — the same one for everyone, inside the assessment environment. You measure what now sets a good software engineer apart: direction, verification and judgment.

Coding test — AI-assisted, like real work
Section: 00:24:10
AI use tracked
Submit
rate_limiter.ts
TypeScript (5.4)
1class TokenBucket {
2 constructor(rate: number, capacity: number) {
3 this.tokens = capacity;
4 this.lastRefill = Date.now();
5 }
6
7 // AI suggestion: refill inside the lock
8 tryAcquire(): boolean {
9 const now = this.refillAndGetTime();
10 this.tokens = Math.min(this.capacity, …);
11 return this.tokens >= 1;
12 }
13}
8 / 8 tests passed
RUN TESTS
AI assistant
Claude ▼
Why is my bucket refilling too fast under concurrent calls?
You call refill() before reading lastRefill — two callers can both see a stale timestamp. Move the timestamp update into the same critical section.
Ask the assistant…
Every prompt reviewed
Prompt qualityStrong
Copy without reviewNone
Iterations with AI4
Bring your AI stack
Your choice
Coodeshbuilt in
Claudeconnected
Codexavailable
Coodesh assistant built in · or connect Claude / Codex · full prompt history in the report
89
AI collaboration — 89%
Prompts, judgment and code all scored

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AI-assisted programming test

What is an AI-assisted coding assessment?

An AI-assisted coding assessment is a technical test where candidates solve a realistic task with an AI assistant, under rules the hiring team sets before sending the invite. On Coodesh it runs in the remote IDE question: a full development environment in the browser, with an editor, a terminal, a running dev server and a live preview, where every candidate gets an isolated copy of the same starter project.

Most engineering teams already write code with AI in the editor, so a test that bans it measures a job that no longer exists. What separates candidates now is how well they steer the tool, verify its output and own the result. That is why reviewers see the process, not just the output: the final code, the commit history and, when the policy allows AI, the candidate's conversation with the agent.

  • Seven base images, from React + Vite to Spring Boot 3.5.
  • AI policy set per question, with per-assessment overrides.
  • Starter project from a public GitHub repo or a .zip up to 5 MB.
  • Environment setup time is added back to the candidate's timer.

How the remote IDE coding test works

  1. Add the starter project

    Pick a framework, then link a public GitHub repository or upload a .zip. Validation checks for a Git project under 5 MB with a README.md and a devfile.yaml at the root.

  2. Choose the AI policy

    No assistant is the default. You can allow AI through your company's credential, through Coodesh's credential, or with no network restriction. When AI is blocked, the prompt says so and assistants are disabled.

  3. Candidates build in the browser

    Each one works on an isolated copy: runs the app and commits, with nothing to install. You can open the same environment yourself before inviting anyone.

  4. Your team reviews and decides

    On submit the environment shuts down and a final snapshot of the project remains. AI grades against your criteria, and the hiring decision stays with your reviewers.

Remote IDE features for AI-allowed technical assessments

Each feature is there to show how the candidate got to the result.

  • Four-level AI policy

    The default is the strictest setting, so an untouched field never enables AI by accident. Override it in a single assessment without editing the library question.

  • Real frameworks

    Vue 3 + Vite, React + Vite, Angular CLI 18, Django 5.2 (Python), Laravel 11 (PHP), .NET 9 (C#) and Spring Boot 3.5 (Java).

  • Commit history

    The Commits tab lists the date, author and message of every commit, so you see the order of decisions and not only the final code.

  • Agent conversation

    When AI is allowed, reviewers get the candidate's conversation with the AI agent and can see what they asked for and how they iterated.

  • Presentation mode

    Ask for a screen recording where the candidate walks through the solution. It shows who drove the tool and who simply accepted suggestions.

  • Criteria-based grading

    Write criteria weighted 1 to 5 and AI scores each one. Extra criteria show up as notes for the reviewer without changing the score.

When to use an AI coding test

  • Teams that already code with AI

    If your engineers use assistants every day, test under the same conditions and see who delivers more, and better, with them.

  • Framework-heavy frontend and backend roles

    Assess React, Vue, Angular, Django, Laravel, .NET or Spring Boot work in context. The standard coding question does not cover frameworks.

  • Senior roles and final rounds

    An open-ended project plus presentation mode surfaces judgment and trade-offs before the team interview.

Try an AI-assisted coding test

Start for free, build a remote IDE question and walk through it yourself before you invite your first candidate.

Frequently Asked Questions

What is a code-assisted assessment, and why does it exist?

It is a technical assessment in which the use of AI assistants is allowed and declared, instead of forbidden and hidden. It exists because the work has changed: most engineering teams already code with Copilot, Claude or Cursor in the editor, and a test that bans AI measures working conditions that no longer exist in the role. The right question is no longer "can this person code without help?" but "does this person deliver more and better with the tools the team uses?". On Coodesh, the company decides per assessment which rule applies, states it in the prompt, and designs the questions to measure what matters under each rule.

How does an AI-allowed assessment work on Coodesh?

Three design decisions. First, an explicit rule: the prompt states that AI assistants are allowed, which removes the advantage of anyone who would cheat and levels the conditions for everyone. Second, the right problem: with AI allowed, classic algorithm exercises lose their point, because AI solves them in seconds; the prompt becomes a problem with context, ambiguity and constraints, where the value lies in the decisions, not the typing. Third, a defense step: in the same assessment, a video question or a conversational AI interview asks the candidate to explain their choices, point out the code's limitations and say what they would change in production. The deliverable shows the result; the defense shows who was driving the tool.

If AI writes the code, what exactly is being assessed?

The skills that remain as a human differentiator, and that today separate a good hire from a bad one: breaking down the problem before prompting, critically evaluating what the AI returned, spotting the subtle mistake the AI made with confidence, integrating the parts into a coherent whole and explaining trade-offs. The question's evaluation criteria are written for exactly that: quality and fit of the final solution, handling of edge cases, and consistency between what was delivered and what the candidate can justify. Flawless code the author cannot explain is worth less than good code defended clearly, and the assessment is built to reveal that difference.

What if the role requires coding without AI? Can that be guaranteed?

It can be made visible, which is what supports a decision. If the assessment rule is no assistants, the integrity features log what gives usage away: leaving the tab and window focus loss, pasting without a prior copy in the same question, text inserted without going through the clipboard (the insert button of assistants and extensions), a second screen and developer tools. The reviewer sees each event on the timeline, with the pasted content, and the browsing recording shows the process. Our recommendation is to use the restricted rule only when the role justifies it, and to tell the candidate why; a ban without purpose only increases drop-off.

Can both rules coexist in the same process?

Yes, and it is the design most often seen in mature processes. A section with AI allowed, with a realistic problem, measures the assisted productivity the role will demand; a short section without AI, covering fundamentals, confirms the foundation behind that productivity; and a video defense or AI interview completes the set. Each section has its own weight in the final score, so the company decides what matters most for that position. The candidate goes through both conditions knowing the rule for each, which is fairer than a single rule that favors only one profile.

Won't candidates find it strange to be allowed to use AI in a test?

Quite the opposite: for the generation that already works this way, it is the test that bans AI that feels outdated, while one that allows it signals how the company really works. What matters is clarity: the rule stated in the invitation and in the prompt, what will be assessed, and a heads-up that there will be a conversation about the solution. The assessment becomes an honest sample of the job, which improves completion rates and the quality of self-selection, because those who do not want to work with AI find out early that the role is not for them, and those who master the tool get a place to show it.

How do I start applying this on Coodesh today?

Pick an open technical role and build the assessment in three blocks: a free-form coding question with a contextualized problem and the AI rule stated in the prompt, a video question or conversational AI interview asking the candidate to defend the solution, and, if the role justifies it, a short fundamentals block with integrity enabled. Define review criteria with judgment in mind, not syntax, and use Try it yourself before sending. Results arrive with the deliverable, the recordings and the scored criteria for your review, and the comparison with your previous process usually becomes clear within one or two roles.

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