Interview scorecard template

Human-Computer Interaction (HCI) Specialist interview scorecard

Evaluate Human-Computer Interaction (HCI) Specialist candidates across 4 weighted areas: technique and experimental design, results that went somewhere, troubleshooting and reproducibility, and documentation and collaboration. Technique and experimental design leads at 35%, so probe how they design studies: within versus between subjects, counterbalancing, sample sizing, and instruments such as SUS, NASA-TLX, think-aloud. Use the rubric to compare role-specific evidence consistently.

See AI scoring
laboratory applied scienceeye trackinginteraction designusability testingwcag accessibility
TL;DR
For technique and experimental design, look for evidence the candidate names the study design and rationale, defends sample size and task order, and distinguishes formative usability work from controlled comparative experiments. For results that went somewhere, look for evidence the candidate cites specific products and measured deltas, plus the recommendation engineers or designers actually implemented after the study. 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

Technique and experimental design

35% weight

Probe how they design studies: within versus between subjects, counterbalancing, sample sizing, and instruments such as SUS, NASA-TLX, think-aloud protocols, eye tracking or Tobii heatmap setups.

Evidence to listen for

  • Runs the assays and instruments themselves rather than describing what a team does
  • Designs experiments with controls, replicates, and a stated hypothesis
  • Knows what each technique can and cannot resolve
  • Understands the science, not only the protocol

Five-point scoring guide

1
Poor

Protocol follower with no experimental design; cannot justify controls.

2
Needs Improvement

Runs standard assays; designs experiments poorly or not at all.

3
Satisfactory

Competent at the bench with sound routine design.

4
Very Good

Designs rigorous experiments and understands the limits of each technique.

5
Excellent

Names the study design and rationale, defends sample size and task order, and distinguishes formative usability work from controlled comparative experiments.

02
Evaluation factor

Results that went somewhere

25% weight

Ask which findings changed a shipped interface: navigation redesigns, error rate drops, task completion or time-on-task gains, WCAG 2.2 fixes that cleared an audit.

Evidence to listen for

  • Names projects where their results changed a decision, a process, or a product
  • States their own contribution rather than the group's
  • Has taken something from bench to a larger scale, a filing, or a publication
  • Knows what happened to the work after they handed it over

Five-point scoring guide

1
Poor

No results that went anywhere; work is entirely exploratory.

2
Needs Improvement

Contributed to projects but cannot say what their data changed.

3
Satisfactory

Real contributions; outcomes described loosely.

4
Very Good

Names results that changed a decision, with clear personal scope.

5
Excellent

Cites specific products and measured deltas, plus the recommendation engineers or designers actually implemented after the study.

03
Evaluation factor

Troubleshooting and reproducibility

25% weight

Test how they handle noisy or contradictory data: pilot failures, participant dropout, confounded prototypes, disagreement between behavioural logs and self-report scores.

Evidence to listen for

  • Treats a failed run as information rather than bad luck
  • Isolates reagent, instrument, operator, and biological causes systematically
  • Knows why a result failed to reproduce and can say when their own data was wrong
  • Keeps records good enough to diagnose from months later

Five-point scoring guide

1
Poor

Repeats failed runs unchanged; no diagnostic thinking.

2
Needs Improvement

Troubleshoots by substitution; cannot explain a reproducibility failure.

3
Satisfactory

Solid troubleshooting on familiar assays.

4
Very Good

Systematic isolation of causes, and honest about their own irreproducible results.

5
Excellent

Describes re-running pilots, triangulating telemetry against interviews, and openly reports where effects did not replicate or were underpowered.

04
Evaluation factor

Documentation and collaboration

15% weight

Look for study protocols, consent and ethics submissions (IRB or equivalent), tagged qualitative codebooks, and how they hand findings to product managers and front-end engineers.

Evidence to listen for

  • Keeps records to the standard the setting requires, whether that is GLP, GMP, or a defensible notebook
  • Writes up so someone else can repeat the work
  • Works with process, quality, or clinical colleagues rather than in a bench silo
  • Explains a result to a non-specialist without overclaiming

Five-point scoring guide

1
Poor

Records would not survive audit; work is not repeatable from them.

2
Needs Improvement

Documentation is thin; write-ups need heavy editing.

3
Satisfactory

Adequate records and write-ups; collaboration is limited.

4
Very Good

Audit-standard records and clear communication across functions.

5
Excellent

Keeps traceable protocols and coded transcripts, and turns them into prioritised, developer-legible recommendations rather than a slide deck of quotes.

Evidence-led prompts

Interview questions for a Human-Computer Interaction (HCI) Specialist

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

  1. 01

    How do you approach usability testing?

  2. 02

    Which methods do you use for gathering user feedback?

  3. 03

    What role does user research play in your design process?

  4. 04

    Can you give an example of a project where you improved the user experience?

  5. 05

    Can you give an example of solving a challenging interaction design problem?

See the complete Human-Computer Interaction (HCI) Specialist 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