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AI is about to walk into your therapy room wearing a lab coat and a confident tone, and the licensing exam is going to ask whether you’ll believe it. I’m Eric Twaktman, and I’m laying out the real skill behind every AI ethics question: holding on to clinical judgment when an algorithm hands you a neat, orderly recommendation that looks “better” than your own work. 

We get clear on what counts as AI in counseling, from progress-note drafting and session summaries to chatbots and automated risk assessments. Then we slow down on the most important concept: AI produces plausible output based on patterns, not understanding of your client’s context. That single idea explains why the NBCC draws a hard line against AI replacing professional judgment or the therapist-client relationship, and why “the software said so” is never a safe defense when client welfare is on the line. 

From there, we walk through the NBCC-aligned principles that let you reason through almost any scenario: accountability, client welfare, AI competence, clinical competence, and confidentiality. We also tackle the tripwires that create real-world complaints and exam wrong answers fast: AI-specific informed consent that is separate and refusable, true de-identification (not just removing names), HIPAA-grade security, secure deletion policies, and algorithmic bias that can distort care across language and culture. A case study of a stressed practicum student shows how fatigue and deference can snowball into multiple ethics violations without anyone intending harm. 

If you’re studying for the exam or building an AI policy for your practice, this gives you a simple filter: AI recommends, we decide, and we stay fully accountable. Subscribe for more exam-ready breakdowns, share this with a classmate or supervisee, and leave a review with the AI ethics question you want us to unpack next.

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This podcast is not associated with the NBCC, AMFTRB, ASW, ANCC, NASP, NAADAC, CCMC, NCPG,  CRCC, or any state or governmental agency responsible for licensure.