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.
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.
