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Day 11 of 20 Β· AI for Recruitment

Structured Interview Scorecards

After a great interview, the hiring manager sends you feedback that says: "Really liked them. Good energy. Probably a yes." Meanwhile, another interviewer writes: "Seemed okay. Not sure about the technical depth."

That is not feedback you can act on. It is not feedback you can compare across candidates. And it is not feedback that protects you from bias claims if someone challenges your hiring decision.

Structured scorecards solve all three problems. Today you will learn how to use AI to generate rubrics that make interview feedback consistent, comparable, and defensible.

Why structured scoring reduces bias

Unstructured interviews are essentially coin flips. Research from the University of Toledo found that interviewers form strong impressions within the first ten seconds β€” and spend the rest of the interview confirming that initial gut feeling.

Structured scorecards counteract this by:

Forcing specific evaluation β€” Instead of "I liked them," interviewers must rate specific competencies: communication, technical depth, problem-solving, leadership. Each one gets its own score.

Anchoring expectations β€” By defining what a 1, 3, and 5 look like for each competency, interviewers evaluate against a standard, not against their personal preferences.

Creating comparability β€” When every candidate is scored on the same rubric, you can do genuine apples-to-apples comparisons instead of comparing vibes.

Building documentation β€” If a hiring decision is ever questioned, structured scorecards provide a clear, consistent record of why each candidate was assessed the way they were.

Knowledge Check
What is the main problem with unstructured interview feedback like "Good energy, probably a yes"?
A
It is too short
B
It does not mention the candidate's salary expectations
C
It is too positive
D
It cannot be compared across candidates, is driven by bias, and does not document specific evaluation criteria
Unstructured feedback captures impressions, not evidence. You cannot compare "good energy" from one interviewer against "solid technical skills" from another. Structured scorecards force every interviewer to evaluate the same competencies on the same scale, creating feedback that is comparable, actionable, and defensible.

AI-generated scorecards from a JD

Here is the prompt that turns any job description into a structured scorecard in under a minute:

The prompt: "Based on the following job description, create an interview scorecard. Include: 5-7 key competencies to evaluate, a 1-5 rating scale for each competency, descriptions of what a 1 (poor), 3 (acceptable), and 5 (exceptional) answer looks like for each competency, a section for overall recommendation (Strong Yes / Yes / Neutral / No / Strong No), and a notes field for each competency. Format it as a clean table."

Paste your job description after the prompt. In 30 seconds, you have a scorecard that would have taken 45 minutes to build manually.

For different interview stages, adjust the focus:

- Phone screen scorecard: Communication, motivation, basic qualification fit

- Technical interview scorecard: Domain expertise, problem-solving approach, technical communication

- Culture interview scorecard: Values alignment, collaboration style, adaptability

- Final round scorecard: Leadership potential, strategic thinking, long-term fit

Interview Scorecard β€” Competencies, Rating Scale, Answer Benchmarks, Overall Recommendation
A structured scorecard turns subjective impressions into comparable data.

Templates for every interview type

Technical interview scorecard prompt:

"Create a technical interview scorecard for a [role]. Include competencies for: [list specific technical skills from the JD]. For each, describe what a junior-level answer, mid-level answer, and senior-level answer looks like. Add a competency for 'explains technical concepts to non-technical stakeholders' β€” rate their ability to simplify."

Behavioral interview scorecard prompt:

"Create a behavioral interview scorecard for a [role]. Competencies should include: [leadership, collaboration, conflict resolution, adaptability β€” pick from JD]. Each competency should have a STAR-quality indicator β€” rate whether the candidate provided a complete Situation-Task-Action-Result story or gave vague generalities."

Leadership assessment scorecard prompt:

"Create a leadership interview scorecard for a [VP/Director/Head of] role. Include: strategic vision, team development, stakeholder management, decision-making under uncertainty, and track record of delivery. For each, describe what 'proven at scale' looks like versus 'theoretical only.'"

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AI Assistant
online
Create an interview scorecard for a Senior Data Analyst role. The JD requirements are: SQL and Python proficiency, experience with data visualization tools (Tableau/Looker), ability to translate data into business recommendations, stakeholder communication, and experience with A/B testing. The interview will be 45 minutes with the Analytics Director.
**Senior Data Analyst β€” Interview Scorecard** | Competency | 1 (Poor) | 3 (Acceptable) | 5 (Exceptional) | Score | Notes | |---|---|---|---|---|---| | **SQL & Python** | Cannot describe basic queries; no evidence of hands-on work | Can write intermediate queries; uses Python for data cleaning and analysis | Writes complex queries (window functions, CTEs); uses Python for automation and statistical analysis | _/5 | | | **Data Visualization** | Limited tool experience; charts lack clarity | Builds clear dashboards; understands when to use different chart types | Creates self-serve dashboards with business context; has driven adoption across teams | _/5 | | | **Business Translation** | Presents data without actionable insight | Connects data findings to business outcomes with clear recommendations | Proactively identifies business opportunities from data; has examples of insights driving revenue or cost savings | _/5 | | | **Stakeholder Communication** | Struggles to explain analysis to non-technical audience | Communicates findings clearly; adjusts language for the audience | Builds trusted advisor relationships; stakeholders proactively seek their input | _/5 | | | **A/B Testing** | Theoretical understanding only | Has designed and analyzed A/B tests; understands statistical significance | Has built A/B testing frameworks; can explain trade-offs between test duration, sample size, and confidence levels | _/5 | | **Overall Recommendation:** ☐ Strong Yes ☐ Yes ☐ Neutral ☐ No ☐ Strong No **Key Strengths:** **Concerns or Gaps:** **Interviewer:** _________________ **Date:** _________
↻ Replay conversation
Knowledge Check
Why should a scorecard include descriptions of what a 1, 3, and 5 answer looks like?
A
To make it easier for HR to audit interviews
B
To make the scorecard longer and more official-looking
C
To anchor interviewers to objective standards so they evaluate consistently, regardless of personal preferences
D
To give candidates hints about what to say
Without anchored descriptions, one interviewer's "3" might be another interviewer's "5." Descriptive anchors ensure everyone is calibrated to the same standard. This is especially important when multiple interviewers assess the same candidate β€” their scores become genuinely comparable.

Making scorecards work in practice

The best scorecard in the world fails if interviewers do not use it. Here are practical tips for adoption:

Send the scorecard before the interview β€” Not after. Interviewers should know what they are evaluating before they walk in.

Set a 24-hour feedback deadline β€” The longer interviewers wait to complete their scorecard, the more their memory fades and their scores drift toward gut feeling. Ask for scorecards within 24 hours.

Debrief with data β€” In the hiring debrief, project the scorecards side by side. Compare scores across interviewers. Where they agree, you have signal. Where they disagree, you have a productive conversation.

Iterate the template β€” After each hiring cycle, ask AI: "Based on this scorecard and the feedback from interviewers, what improvements would make this more useful for the next round?" Scorecards should evolve with your team's experience.

Final Check
When should interviewers receive the scorecard?
A
After the interview, along with the candidate's thank-you note
B
Before the interview, so they know what to evaluate during the conversation
C
They should create their own scorecard based on what they think is important
D
Only after all candidates have been interviewed
Sending the scorecard beforehand ensures interviewers walk in with a clear evaluation framework. They know which competencies to probe, what good answers sound like, and how to score consistently. Without it, they default to unstructured conversation and gut-feel assessments.
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Day 11 Complete
"Structured scorecards turn interviews from subjective gut checks into comparable data. AI generates them from any JD in 30 seconds β€” there is no reason to interview without one."
Tomorrow β€” Day 12
LinkedIn vs Job Spec β€” Candidate Fit at a Glance
Tomorrow you'll build a reusable prompt that compares any LinkedIn profile against any job spec β€” giving you a fit score, matches, and gaps instantly.
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1 day streak!