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As of 13 August 2026, AI can only partly score candidates' interview answers.
This still needs a person who signs their name to it.
Can you do it?
5 minutesto a draft.
30 minutesto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ita colleague
What the alternative costsThe supplied tool data gives no price for a human alternative to candidate scoring.
If this goes wrong: a weak or biased score can remove a suitable candidate and expose the employer to a complaint or legal claim.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.
How to actually do it
- Open the role description and the interview plan, then write down the job-relevant criteria and the permitted score for each level.
- Remove irrelevant personal information from the transcript where practical, and obtain the candidate's answers in the same format for every candidate.
- Paste the role, questions, rubric and one candidate's answers into the prompt, keeping the rubric unchanged between candidates.
- Ask the model to produce the evidence, criterion scores, uncertainties and unsupported inferences in the requested table.
- Compare every score with the exact answer and rubric, changing any score that lacks quoted evidence or uses a criterion inconsistently.
- Have at least one hiring colleague or qualified HR professional compare the candidates' evidence and approve the process before the scores influence a decision.
Prompt
Score the interview answers below against the assessment rubric, but do not make the hiring decision. Role: [ROLE] Interview questions: [PASTE QUESTIONS] Scoring rubric: [PASTE THE AGREED CRITERIA AND SCORE DEFINITIONS] Candidate answers: [PASTE THE TRANSCRIPT OR ANSWERS] For each criterion, give: 1. The score allowed by the rubric. 2. The exact evidence from the candidate's answer. 3. A short explanation linking the evidence to the criterion. 4. Any uncertainty or missing evidence. Do not infer ability, personality, health, disability, age, race, religion, sex, sexual orientation, gender reassignment, pregnancy, marital status or other protected or personal characteristics. Do not score accent, fluency, confidence, style or similarity to the interviewer unless the rubric explicitly makes a job-relevant communication requirement and defines how it is measured. Do not use information outside the answers and rubric. Flag criteria that are ambiguous, unsupported or applied inconsistently. End with a table of criterion scores and a separate list of points that a hiring panel must check before using this assessment. State clearly that the output is a draft assessment for human review, not a hiring decision.
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot decide whether an ambiguous answer demonstrates the level of judgement the role needs.
- AI cannot reliably detect every indirect source of bias in the questions, rubric, transcript or scoring process.
- AI cannot take legal responsibility for an employment decision or defend the process to a candidate.
- AI cannot replace a trained panel's knowledge of the role, reasonable adjustments and relevant recruitment context.
- AI can make a speculative interpretation look like a measured score, especially when an answer is incomplete.
What caps this at PARTLY: legal accountability, judgement under ambiguity and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 2 |
| Verification | 1 |
| Liability | 0 |
| Effort delta | 2 |
| Total | 7 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can AI score interview answers?
- Partly. AI can apply a supplied rubric to transcripts and show evidence for provisional scores, but a human must check the reasoning and own the hiring decision.
- Is it legal to use AI to score job interviews in the UK?
- It depends on the process, data, criteria and safeguards, so do not treat an AI score as a legal clearance. This is not professional advice; a serious case needs a qualified HR professional or an employment solicitor.
- Can AI rank candidates after an interview?
- It can produce a ranking from the criteria and scores you provide, but the ranking may reproduce a biased rubric or unsupported interpretation. Use it as a review aid and have the hiring panel check the evidence before making a decision.
- How do I use AI to score interview answers fairly?
- Give it the same job-related rubric, questions and scoring scale for every candidate, and require evidence from the answers rather than impressions. Check for missing evidence, inconsistent treatment and protected-characteristic inferences with a hiring colleague or qualified HR professional.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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