Interview scorecard template

Human-AI Collaboration Facilitator interview scorecard

Evaluate Human-AI Collaboration Facilitator candidates across 4 weighted areas: subject and technical command, how they actually teach, group management and safeguarding, and progress and communication. Subject and technical command leads at 30%, so check fluency with the tools they will coach on: ChatGPT Enterprise, Copilot, Claude, retrieval setups, prompt patterns, evaluation. Use the rubric to compare role-specific evidence consistently.

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education coachingai adoptionchange managementprompt engineeringworkshop facilitation
TL;DR
For subject and technical command, look for evidence the candidate names models and features precisely, explains failure modes such as context loss or hallucination, and maps each tool to concrete team tasks. For how they actually teach, look for evidence the candidate describes a real curriculum with formats, cohort sizes, artefacts left behind, and adjustments made after a session landed badly. 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

Subject and technical command

30% weight

Check fluency with the tools they will coach on: ChatGPT Enterprise, Copilot, Claude, retrieval setups, prompt patterns, evaluation of outputs, and where hallucination risk bites specific workflows.

Evidence to listen for

  • Knows the material or discipline well beyond the level they teach
  • Holds the qualifications, licences, or certifications required, and they are current
  • Can answer an unexpected question honestly rather than bluffing
  • Keeps learning in their own field

Five-point scoring guide

1
Poor

Knowledge barely ahead of the learners; bluffs when asked something unexpected.

2
Needs Improvement

Adequate on the syllabus only; gaps show under questioning.

3
Satisfactory

Solid command of the material for the level taught.

4
Very Good

Depth well beyond the taught level; comfortable being asked anything.

5
Excellent

Names models and features precisely, explains failure modes such as context loss or hallucination, and maps each tool to concrete team tasks.

02
Evaluation factor

How they actually teach

30% weight

Probe their session design: pilot cohorts, live prompt clinics, paired working sessions, playbooks and prompt libraries they built, plus how they handle sceptics and power users in one room.

Evidence to listen for

  • Describes how they teach a specific thing, not their philosophy of teaching
  • Adapts when the first explanation does not land
  • Checks understanding rather than assuming it
  • Differentiates for varying ability within one group

Five-point scoring guide

1
Poor

Only philosophy, no method; one explanation and no plan B.

2
Needs Improvement

Delivers content but cannot adapt when learners are lost.

3
Satisfactory

Sound instruction; differentiation is limited.

4
Very Good

Multiple routes to the same concept, with real checks for understanding.

5
Excellent

Describes a real curriculum with formats, cohort sizes, artefacts left behind, and adjustments made after a session landed badly.

03
Evaluation factor

Group management and safeguarding

25% weight

Assess how they handle confidential data in exercises, job-security anxiety, shadow AI use, and organisational AI policy or EU AI Act obligations during workshops.

Evidence to listen for

  • Has a concrete approach to a disruptive individual and a whole group losing focus
  • Knows the safeguarding or duty-of-care obligations for this age group and setting
  • Escalates a concern through the right channel
  • Sets boundaries without becoming punitive

Five-point scoring guide

1
Poor

No behaviour strategy; unaware of safeguarding obligations.

2
Needs Improvement

Struggles with disruption; vague on duty of care.

3
Satisfactory

Manages a normal group; less confident with serious disruption.

4
Very Good

Confident group management with clear safeguarding awareness.

5
Excellent

Sets ground rules on data before exercises, surfaces job-loss fears openly, and escalates unsanctioned tool use through policy rather than shaming users.

04
Evaluation factor

Progress and communication

15% weight

Look for how they proved adoption stuck: usage telemetry, time saved per workflow, quality reviews of AI output, and reporting back to sponsors or department heads.

Evidence to listen for

  • Measures whether learners actually improved, not just attended
  • Gives feedback that changes what someone does next
  • Communicates with parents, clients, or managers about difficult progress honestly
  • Records progress usefully

Five-point scoring guide

1
Poor

No sense of whether anyone improved.

2
Needs Improvement

Tracks attendance rather than progress; avoids hard conversations.

3
Satisfactory

Monitors progress; feedback is general.

4
Very Good

Measures progress concretely and handles difficult conversations well.

5
Excellent

Cites baseline and post-rollout numbers, distinguishes logins from real workflow change, and reports honestly where adoption stalled.

Evidence-led prompts

Interview questions for a Human-AI Collaboration Facilitator

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

  1. 01

    Can you give examples of projects where you facilitated work between people and AI tools?

  2. 02

    Describe a time when AI produced insight that changed a project, and your role in it.

  3. 03

    How do you train and support team members in using these tools?

  4. 04

    How do you decide which tasks suit AI and which need a person?

  5. 05

    What is your process for selecting the right tools for a specific project?

See the complete Human-AI Collaboration Facilitator question set
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