Sensitivity
Catches real defects
Did the candidate identify seeded problems that were actually present?
AI-era technical hiring
See whether an engineer knows what AI output to trust.
Generating code is cheap. Justified confidence is scarce. CodeArena combines executable work, seeded correctness checks, candidate reasoning, and human review so hiring teams can inspect how a candidate builds and verifies.
Known-answer evidence
Executable checks and seeded defects
Confidence through action
Trust or Challenge with a fixed budget
Human decision record
Evidence and follow-up stay reviewable
The scarce skill
Candidates decide whether to trust or challenge an AI-generated implementation. When they challenge it, a scarce verification budget makes diffuse flagging visible and costly while showing where they see the most risk.
Illustrative candidate interaction
Allocate a fixed 10-point decision budget
Assign suspicion to flagged lines. Unallocated points record trust in the output.
2 points remain as trust
The submitted record derives an output-correct estimate of 20% from that remainder.
This demonstrates the shipped decision-budget interaction. It does not represent a final hiring score or the future AI-baseline metric.
One reviewable record
The workflow is designed to reduce interface surprise, preserve the reasoning behind each decision, and give interviewers a precise next question instead of restarting the interview from zero.
01
For AI Critique screens with rehearsal enabled, candidates can practice Trust or Challenge before assessed work begins.
02
A configured screen can combine executable work with seeded AI-output checks, so a polished final answer is not the only evidence.
03
Hiring teams inspect tests, decisions, suspicion allocations, reasoning, and available integrity context before sharing an outcome.
04
Where evidence is available, the report can turn missed defects and uncertain calls into prompts for live validation.
Discernment evidence
Discernment measures judgment about correctness under uncertainty. It does not pretend to replace the rest of an interview loop.
Sensitivity
Did the candidate identify seeded problems that were actually present?
Specificity
Did they leave correct code alone instead of challenging everything?
Confidence evidence
Where captured, the fixed decision budget records what the candidate trusted and where they concentrated suspicion.
The sample report shows how task results, AI Critique findings, integrity context, candidate-safe feedback, and live follow-up fit together.
CodeArena is not a claim that one assessment should answer every hiring question. It is a reviewable evidence layer for the part of engineering work AI made harder to judge.
Repository work is useful for finalist depth. Earlier in the funnel, bounded tasks with seeded truth make the evidence easier to compare and reduce the amount of company context candidates must absorb before they can show judgment.
Yes when the assessment is configured for AI use. CodeArena measures what happens after generation: whether the candidate challenges weak output, verifies important claims, and can explain what deserves trust.
No. It is one structured evidence layer for technical judgment. Coding, system thinking, communication, role context, and human review still matter.
A reviewable record containing the available task results, candidate decisions and reasoning, seeded-defect outcomes, reviewer notes, and focused follow-up prompts. Replay and integrations depend on the selected workflow and plan.
The 15-minute walkthrough maps the role, candidate path, evidence packet, and focused live validation prompts your team would use.