Interview scorecard template

AI-Powered Mental Health Counselor interview scorecard

Evaluate AI-Powered Mental Health Counselor candidates across 4 weighted areas: clinical competence, patient safety and protocol, patient communication, and working in a clinical team. Clinical competence leads at 35%, so check licensure (LPC, LCSW, LMFT) and modality depth: CBT, DBT skills coaching, motivational interviewing, plus how they adapt protocols when an AI. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For clinical competence, look for evidence the candidate names active state licensure, cites specific modalities used per presentation, and describes clinically integrating AI-generated summaries or PHQ-9 trends into treatment planning. For patient safety and protocol, look for evidence the candidate walks through a real escalation from AI flag to human contact, cites duty-to-warn thresholds, and never treats an algorithmic risk score as final. 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

Clinical competence

35% weight

Check licensure (LPC, LCSW, LMFT) and modality depth: CBT, DBT skills coaching, motivational interviewing, plus how they adapt protocols when an AI triage tool or symptom tracker feeds them client data.

Evidence to listen for

  • Command of the procedures, anatomy, and equipment the role requires
  • Holds current registration or certification
  • Knows normal from abnormal and what to do about each
  • Recognises when a case is outside their scope

Five-point scoring guide

1
Poor

Unsafe knowledge gaps; registration missing or lapsed.

2
Needs Improvement

Knowledge gaps that would affect patient care.

3
Satisfactory

Competent for standard cases; needs support on complex ones.

4
Very Good

Strong clinical knowledge; safe and reliable across the usual range.

5
Excellent

Names active state licensure, cites specific modalities used per presentation, and describes clinically integrating AI-generated summaries or PHQ-9 trends into treatment planning.

02
Evaluation factor

Patient safety and protocol

30% weight

Probe suicide and self-harm risk workflows: C-SSRS or Columbia screening, safety planning, escalation when a chatbot flags crisis language, and HIPAA handling inside telehealth and AI note platforms.

Evidence to listen for

  • Follows identification, infection control, and documentation protocol without prompting
  • Can describe an error or near miss and what they did
  • Escalates deterioration early
  • Treats protocol as protection rather than bureaucracy

Five-point scoring guide

1
Poor

Casual about protocol; would not report an error.

2
Needs Improvement

Inconsistent protocol adherence; slow to escalate.

3
Satisfactory

Follows protocol reliably; documentation sometimes thin.

4
Very Good

Protocol is instinctive; escalates early and reports honestly.

5
Excellent

Walks through a real escalation from AI flag to human contact, cites duty-to-warn thresholds, and never treats an algorithmic risk score as final.

03
Evaluation factor

Patient communication

20% weight

Assess rapport built over video and asynchronous chat: how they disclose AI involvement, obtain consent for session recording or transcription, and repair trust when a client distrusts the technology.

Evidence to listen for

  • Explains a procedure to an anxious or confused patient
  • Handles distress, pain, or refusal without losing control of the interaction
  • Respects privacy and dignity in practice, not just in principle
  • Works with families and carers

Five-point scoring guide

1
Poor

Dismissive of patients; no bedside awareness.

2
Needs Improvement

Task-focused; struggles with distressed patients.

3
Satisfactory

Adequate rapport; less confident in difficult interactions.

4
Very Good

Calm, clear, and respectful with anxious or difficult patients.

5
Excellent

Explains AI use in plain client-facing language, secures informed consent explicitly, and gives an example of restoring a client's trust after tooling discomfort.

04
Evaluation factor

Working in a clinical team

15% weight

Look for coordination with prescribers, care managers, and product or clinical engineering teams; ask about supervision hours, case consultation, and reporting model errors or unsafe AI outputs.

Evidence to listen for

  • Hands over cleanly and completely
  • Challenges a colleague when patient safety requires it
  • Takes direction from clinicians without deferring blindly
  • Handles shift work and pressure without becoming difficult to work with

Five-point scoring guide

1
Poor

Poor handover; cannot work in a clinical team.

2
Needs Improvement

Handover gaps; avoids raising concerns about colleagues.

3
Satisfactory

Reliable team member; handover adequate.

4
Very Good

Clean handovers and willing to speak up on safety.

5
Excellent

Describes routine consultation, warm handoffs to psychiatry, and a concrete instance of flagging a flawed AI output to the product or clinical safety team.

Evidence-led prompts

Interview questions for a AI-Powered Mental Health Counselor

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

  1. 01

    What are your qualifications, and where are you registered to practise?

  2. 02

    What experience do you have delivering counselling within a technology-assisted service?

  3. 03

    Which client groups and presentations do you have the most experience with?

  4. 04

    How do you handle a system suggestion that conflicts with clinical guidance?

  5. 05

    How do you judge whether an automated assessment of a client is accurate?

See the complete AI-Powered Mental Health Counselor question set
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