Interview scorecard template

Augmented Intelligence (AI) Experience Designer interview scorecard

Evaluate Augmented Intelligence (AI) Experience Designer candidates across 4 weighted areas: portfolio, craft and rationale, feedback and iteration, and working with the brief. Portfolio leads at 35%, so review shipped AI interfaces: chat assistants, copilots, recommendation surfaces. Ask what they designed for confidence display, citation, fallback states, and how usage or task. Use the rubric to compare role-specific evidence consistently.

See AI scoring
design visual communicationai uxconversational designhuman in the loopprompt patterns
TL;DR
For portfolio, look for evidence the candidate shows live AI products with before and after task success or trust metrics, plus the flows they personally owned. For craft and rationale, look for evidence the candidate explains design choices in terms of model behaviour, uncertainty, and user recovery paths rather than visual preference alone. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Portfolio

35% weight

Review shipped AI interfaces: chat assistants, copilots, recommendation surfaces. Ask what they designed for confidence display, citation, fallback states, and how usage or task success changed.

Evidence to listen for

  • Work exists and can be looked at, not just described
  • States what they made versus what the team or a template made
  • Shows range rather than one repeated style
  • Can walk through a piece from brief to final

Five-point scoring guide

1
Poor

No portfolio, or work that is unattributable or clearly templated.

2
Needs Improvement

Thin portfolio; unclear what they personally made.

3
Satisfactory

Real work with adequate range; contribution mostly clear.

4
Very Good

Strong varied portfolio with clear personal ownership.

5
Excellent

Shows live AI products with before and after task success or trust metrics, plus the flows they personally owned.

02
Evaluation factor

Craft and rationale

25% weight

Probe craft in probabilistic UX: error and hallucination states, streaming responses, prompt affordances, model latency handling, Figma prototypes wired to real or mocked LLM output.

Evidence to listen for

  • Explains why a layout, type choice, or colour decision serves the brief
  • Knows typography and hierarchy as craft, not decoration
  • Works to a brand system without either breaking it or hiding behind it
  • Names the tools they are genuinely fast in

Five-point scoring guide

1
Poor

Cannot explain any decision; work is arbitrary.

2
Needs Improvement

Talks in taste terms only; no link between choice and brief.

3
Satisfactory

Sound craft with some ability to justify decisions.

4
Very Good

Articulate about why each choice serves the brief.

5
Excellent

Explains design choices in terms of model behaviour, uncertainty, and user recovery paths rather than visual preference alone.

03
Evaluation factor

Feedback and iteration

25% weight

Assess how they tested AI features: wizard-of-Oz sessions, red-teaming prompts with users, reviewing conversation transcripts, and what they changed after seeing users mistrust or over-trust output.

Evidence to listen for

  • Takes critique without treating it as an attack
  • Distinguishes a subjective preference from a real problem, and says so politely
  • Iterates fast rather than defending version one
  • Delivers files correctly and on time

Five-point scoring guide

1
Poor

Defensive about critique; will not revise.

2
Needs Improvement

Accepts feedback passively; iterations do not improve the work.

3
Satisfactory

Revises willingly; struggles to push back on weak feedback.

4
Very Good

Iterates quickly and can argue for the work when the feedback is wrong.

5
Excellent

Cites specific transcript or usability findings that forced a redesign, including features they removed or gated.

04
Evaluation factor

Working with the brief

15% weight

Look for work with ML engineers and product on what the model can actually do: dataset limits, evaluation criteria, responsible AI or transparency guidelines they helped write.

Evidence to listen for

  • Asks about audience and goal before opening the design tool
  • Works with marketing, product, or clients rather than in isolation
  • Flags an impossible brief early
  • Hands over files and assets others can actually use

Five-point scoring guide

1
Poor

Designs in isolation; ignores the brief's purpose.

2
Needs Improvement

Starts designing before understanding the goal.

3
Satisfactory

Asks the right questions when prompted.

4
Very Good

Interrogates the brief up front and hands over cleanly.

5
Excellent

Translates model constraints into scoped design decisions and negotiates feasibility with engineers using shared evaluation language.

Evidence-led prompts

Interview questions for a Augmented Intelligence (AI) Experience Designer

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe an AI project you contributed to and your role in it?

  2. 02

    Have you redesigned an AI interface based on user feedback?

  3. 03

    What challenges have you faced designing these experiences?

  4. 04

    What is your process for designing interfaces for these systems?

  5. 05

    How do you handle a feature that does not produce the expected results?

See the complete Augmented Intelligence (AI) Experience Designer question set
Put this rubric to work

Score every candidate against the same standard

Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.

Explore AI Scorecards