Q01
Message
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Post-Interview Candidate Evaluation Scorecard
A structured scorecard for interviewers to rate a candidate right after a screen, panel, or onsite round — covering core competencies, standout strengths, and a hire recommendation. An AI follow-up interview digs into the reasoning behind borderline scores so hiring committees get real evidence, not just a number.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
11 questions · ~6 min
Q02
Short TextRequired
Which role or position did you interview this candidate for?
Q03
Multiple ChoiceRequired
Which stage of the interview process was this?
- Recruiter phone screen
- First-round or panel interview
- Onsite or final-round interview
- Reference or background check conversation
Q04
MatrixRequired
Rate the candidate on each of the following, based only on what you directly observed in this interview.
5 rows × 5 columns
- Communication & clarity
- Role-specific technical skill
- Problem-solving approach
- Collaboration & teamwork signals
- Culture and values alignment
Columns: Poor · Below expectations · Meets expectations · Above expectations · Exceptional
Q05
Opinion ScaleRequired
How likely are you to recommend advancing this candidate to the next stage (or extending an offer)?
Scale: 0 – 10
Min:Would not advanceMax:Would advance enthusiastically
Q06
Best–Worst Trade-off (MaxDiff)Required
Across this interview, which of these signals stood out as the strongest, and which stood out as the weakest?
- Technical depth
- Communication clarity
- Problem-solving under pressure
- Collaboration signals
- Culture and values fit
- Motivation and interest in the role
- Leadership potential
Pick best & worst per setBest:Strongest signalWorst:Weakest signal
Q07
Long Text
What specific example or moment from the interview best supports your evaluation? Concrete details help other interviewers calibrate.
Q08
Long Text
What concerns, if any, would give you pause about this candidate?
Q09
AI Interview
Reconstruct the concrete evidence behind this interviewer's overall recommendation, not just the score. If the recommendation score was in the middle range (roughly 4-7), probe specifically what pushed it up or down and whether the gaps mentioned are coachable versus dealbreakers. Ask for a specific example that would change their mind in either direction, and check whether their rating matches the concerns and strengths they described in the open text answers.
Q10
Multiple ChoiceRequired
What is your final recommendation for this candidate?
- Strong yes - hire
- Yes - advance to next step
- Maybe - need more information
- No - do not advance
- Strong no - clear reject
Q11
Message
That's everything — thank you for the thoughtful feedback! Your scorecard will be shared with the hiring committee alongside notes from other interviewers to inform the final decision.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Combines structured competency ratings (matrix), a hire/no-hire recommendation, and a MaxDiff ranking of standout signals in one scorecard, so committees see both scores and prioritized evidence.
- An AI follow-up interview automatically probes the reasoning behind borderline or inconsistent scores, turning a static rating into documented evidence instead of just a number.
- Open-ended prompts capture the specific example that most supports the evaluation and any concerns that would give the interviewer pause, reducing vague 'thumbs up/down' feedback.
- Auto-generated reports and transparent prompts mean hiring teams can see exactly what was asked and why, with no manual write-up required from the interviewer.
Jotform
Candidate Evaluation Form TemplateA fielding-ready, customizable candidate evaluation form built on Jotform's drag-and-drop form builder. It's designed for quick post-interview rating capture rather than structured hiring-committee workflows. Good for simple scorecards but not built specifically around interview-stage nuance or evidence-gathering.
What it does well
- Easy to customize with Jotform's form builder and templates library
- Quick to deploy for basic post-interview rating capture
- Integrates with Jotform's broader form ecosystem (e.g., approval flows, storage)
Where it falls short
- Static form fields only — no adaptive follow-up questioning to probe the reasoning behind a rating
- No mechanism to automatically surface or reconstruct evidence behind a recommendation
- No published methodology or transparent prompt logic for how evaluations are structured
SurveyMonkey
Streamline Hiring With A Candidate Evaluation Form TemplateSurveyMonkey offers a ready-to-use candidate evaluation template aimed at streamlining interviewer feedback collection. It covers standard rating fields but is a generic survey template rather than one tailored to interview-stage evidence or hiring-committee needs. Reporting is limited to SurveyMonkey's standard analytics dashboards.
What it does well
- Simple, recognizable survey format that's fast for interviewers to complete
- Backed by SurveyMonkey's established survey distribution and analytics tools
- Template can be customized with additional rating questions
Where it falls short
- No adaptive AI follow-up — static question set can't dig deeper into borderline scores
- No voice AI or guided task/screen-share options for richer interview capture
- Reporting relies on generic survey analytics rather than an evidence-focused hire recommendation report
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.