AI Interview Scorecard Template With Examples

AI Interview Scorecards: Templates and Examples

An interview scorecard turns a conversation into a set of job-related observations that recruiters can review consistently. A good scorecard is not just a number: it shows what was assessed, what evidence supports the rating and where follow-up is needed.

What belongs in a scorecard?

Start with five fields: role and candidate identifier, assessment criteria, question references, evidence from responses and reviewer comments. Keep personally sensitive information out of the scoring criteria.

Example: customer support role

Use a 1–5 scale for each relevant skill.

Communication: Explains a problem clearly and checks understanding. Problem solving: Works through a realistic service issue step by step. Empathy and judgment: Responds respectfully to a frustrated customer. Process knowledge: Knows when to escalate and document a case. Role readiness: Provides relevant examples and recognizes where more training is needed.

Score anchors should be defined before interviewing. For example, a 1 may mean the answer provides little relevant evidence, 3 sufficient evidence, and 5 strong specific evidence. Treat these as illustrative anchors, not universal measures.

Sample scorecard template

For each criterion, record a weight, a rating from 1 to 5, the evidence from the candidate's answers and any follow-up needed.

Communication (25%): Rating, evidence, follow-up needed. Problem solving (30%): Rating, evidence, follow-up needed. Customer judgment (25%): Rating, evidence, follow-up needed. Process awareness (20%): Rating, evidence, follow-up needed.

Weights should reflect a documented job analysis. Do not allow numerical rankings to conceal missing evidence or unfair criteria.

How AI can assist

AI interview tools may organize transcripts, summarize answers and help present evaluations. A recruiter should confirm the underlying answers and assess whether the scoring rules are appropriate for the position.

Audit and improve

Periodically review whether criteria predict job-relevant performance, whether the prompts are understandable and whether evaluations are consistent across reviewers.