A take-home used to tell you something. Someone spent a weekend on it, and what came back was a fair proxy for how they think, because producing it required thinking. That proxy is gone. Hand the same take-home to anyone with a model and a couple of hours, and the work comes back clean, structured, and hard to tell apart from the work of a person who could not have written a line of it on their own.
That holds for most of the work an individual contributor hands in. The portfolio from a designer, the sample project from an analyst, the tidy pull request from an engineer, the writing sample from a marketer: each used to be evidence because building it took the capability you were hiring for. Now a candidate can steer a model to any of them and understand little of what came out.
For the person hiring, that is a scoring problem. Using AI is the job now, so the question you are answering is harder than it used to be: when the output is this good and this fast, how much of the judgment behind it is the candidate’s own?
Why the finished artifact stopped proving the person
Start with why this changed so fast. In Metaview’s 2026 AI & Hiring Alignment Report, surveying 505 recruiting leaders and hiring managers across North America and EMEA, 85% of companies that beat their hiring goals use AI in hiring. AI is now how the work gets done, which means it is how candidates produce the very work you use to judge them.
Follow that through and the old evidence stops holding. A take-home, a portfolio, a writing sample, a pull request, each was a rough proxy for capability because producing it took the capability. Now the producing and the understanding have come apart. Someone can ship a strong artifact with a shaky grasp of it, and someone with deep judgment can ship the same artifact in a tenth of the time. On the page, from the outside, the two look identical.
You were always hiring for what sits underneath the artifact: can this person make good calls in the messy middle of the work? Marisa Uranga Bradwell, Director of Recruiting Ops at Deliveroo, keeps the focus where it belongs:
It’s not just the hours saved, but also the time you can use to focus on what really matters: getting the signal we need to make the best hiring decisions.”
What a candidate can tell you about the work now carries the evidence the work itself used to carry.
The four levels of AI-augmented output
Before your panel can score AI-augmented work the same way twice, everyone needs the same picture of what the levels look like. This rubric reads the work through one lens: how much of the judgment behind it belongs to the candidate. It runs from output they cannot account for to output they improved on, and it holds for engineering, design, analysis, or writing.
| Level | What you see in the walkthrough | What it tells you about the candidate |
|---|---|---|
| 1. Can’t rebuild it | Describes what the artifact does but not why it is built that way. Stalls when you ask them to change one assumption. Treats the model’s output as a finished thing they collected. | The output is the model’s, and so is the judgment. Fine for throwaway work, a real risk anywhere the reasoning has to be theirs. |
| 2. Can explain it, didn’t shape it | Understands the output and can walk you through it, but every meaningful choice was the model’s default. Took what it produced and tidied the edges. | Competent, and easy to overrate. They can operate the tool, but you have not watched them make a hard call yet. |
| 3. Directed it | Set the constraints, caught where the model went wrong, and made real tradeoffs. Can point to the parts that are theirs and the parts that are the model’s. | The judgment is there. Strong for most roles, because the work gets better when they are the one steering. |
| 4. Out-judged it | Used the model to move fast and still overrode it where its default was wrong for this context. Can name the better path and why the obvious one fails here. | The hire you hold out for. They get the speed of AI and keep the judgment the model does not have. |
Most candidates land at Level 2 or Level 3, and from a finished artifact alone the two are indistinguishable. Telling them apart is the whole job of the interview, and it is where the rarer Level 4 shows itself.
How to score it: run the walkthrough
You cannot read judgment off a document. You get it by watching someone reason about their own work in real time, where a model cannot fill the gaps for them. Three moves do most of the work, and none of them needs a new take-home.
Make them rebuild one decision live
Pick one non-obvious choice in what they submitted and ask them to reconstruct it from scratch. ‘Why this approach here, and not the simpler one?’ A candidate who made the call answers in seconds. A candidate who took the model’s default has to reverse-engineer a reason on the spot, and you can hear the difference.
Follow the reasoning behind the work
Stop grading the thing they handed in and start grading the thinking around it. Ask what they tried and threw away, where the model was confidently wrong, and what they would change with another day.
Strong candidates have a running commentary on their own work. Weaker ones can only defend its surface.
Probe the road they didn’t take
Ask about the option they rejected. ‘What was the obvious alternative here, and why didn’t you go with it?’
Judgment is mostly about the paths not taken, and it is the hardest thing to fake, because it lives in the reasoning that never made it into the artifact.
The catch is that the signal lives in the texture of the answer, and that texture is the first thing you lose by the time you sit down to score. It helps to have the conversation on the record.
Metaview’s interview notes captures every spoken word, so the moment a candidate reconstructed a tradeoff, or couldn’t, is there to score against instead of reassembled from memory two days later.
Score from what was said
Judgment is hard to score fairly because a great artifact throws a halo. The candidate who handed in the cleanest project feels like the strongest hire, even when the walkthrough said otherwise. The fix is to score from evidence, on the same rubric, for everyone. When the interview is captured, you can point to the exact moment a candidate reasoned through a tradeoff, or reached for the model instead, and agree on a level with the evidence in front of the room.
Consistency across the panel is what turns a rubric into more than paperwork. Matthias Schmeisser, Global Senior Director of TA at emnify, describes the shift once the scoring runs on structure:
I’ve fallen in love with features like Candidate Comparison which allows me to compare candidates based on our interview structure, and also with having an AI assistant that deep dives on the scorecard. It’s just next level.”
Build it into how your team hires
None of this needs a new stage. Replace the graded take-home with a short walkthrough of work the candidate already did, score it on the four-level rubric, and add judgment under AI to the scorecard the way communication or ownership already sit there.
Let interview notes capture the walkthrough, and let reports show which questions your interviewers actually covered across the pipeline. The same reasoning applies when an agent does the first pass on sourcing: you judge the shortlist with its reasoning attached.
If you want the groundwork first, our writeups on quality of hire and what separates a great interviewer set the bar this fits inside, and the Alignment Report has the data behind the shift.
This is the question every hiring team is working through right now, and the answer is more hopeful than the panic suggests:
AI closed the gap on producing the artifact. It did not close the gap on the judgment behind it, and that gap is exactly what a good interview measures. Score for it, and you are hiring the people who get more valuable as the models get better.
See what your candidates really did.
Metaview captures the walkthrough, structures it against your rubric, and lets your panel score AI-augmented work from what a candidate actually said, so a polished output never gets the benefit of the doubt.
Frequently asked questions
What if a candidate did not use AI on the work you are reviewing?
The walkthrough works the same way. Judgment shows up whether the first draft came from a model or from a blank page, so you are still asking whether they can account for the decisions in the work and defend the calls they made. Someone who did the thinking answers with the same fluency no matter what produced the draft, and the tool they reached for matters far less than whether the reasoning is theirs.
Does scoring AI-augmented work differ for engineers, designers, and writers?
The method stays the same across roles; what changes is the decision you pick to walk through. For an engineer that might be a data-model tradeoff; for a designer, a layout or flow choice; for a writer, a structural call about what to cut. You run the same three moves and let the candidate reconstruct a decision that is specific to their craft.
What if the candidate has nothing they can walk you through?
Give them a small, realistic task to do with a model while you watch, then run the same three moves over what they just produced. You are scoring the reasoning as it happens, so a fresh piece works as well as something they brought. It also removes any question of who really did the work.
How much AI use is too much on a take-home?
There is no ceiling worth setting. Someone can lean on a model for most of the drafting and still show more judgment than a candidate who typed every line by hand, because the score comes from the calls they can defend under questioning. Set the task so it is only strong when the human made good decisions, then read for those decisions.
How do you get a new interviewer scoring on the four-level scale?
Have them score a recorded walkthrough on their own before they see how the panel scored it. Where their level differs from everyone else's is exactly where their read of judgment needs calibrating, and you can go back to the specific answer to talk it through. Two or three rounds of that and a new interviewer lands in the same place as the rest of the panel.
