Episode Details

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In this episode, I step back from the nuts and bolts of AI tools and dig into the ethical, supervisory, and professional development challenges of using AI in psychological assessment. I share insights from aviation, medicine, and radiology to highlight risks like automation bias, skill decay, and over-reliance on AI. I also discuss how clinicians and trainees can protect their critical thinking, balance workload reduction with deliberate practice, and frame AI as a thought partner rather than a replacement for judgment. This conversation is designed to spark reflection on how AI is shaping not just our workflows, but our ethics, training practices, and professional identity as psychologists.

Main topics covered:

• Does reducing cognitive load always help—or can it erode clinical skill?

• Strategies to prevent skill decay and maintain clinical proficiency
• Over-reliance, automation bias, and how to resist it
• Structured reflection exercises to improve judgment
• The role of deliberate practice in training and supervision
• Using AI as a thought partner versus a worker
• How AI-supported education can preserve skill development pathways
• Frameworks for trainees: explain-to-verify and reflective reasoning

Cool Things Mentioned

• The Testing Psychologist mastermind groups and business consulting
• “Panel on AI & Testing (AAPdN Annual Conference, April 2025)”: https://www.theaapdn.org/
• “We’re All Wrong Sometimes: Blindspots, Biases, & Getting Better w/ Dr. Stephanie Nelson (The Testing Psychologist #276)”: https://thetestingpsychologist.com/276-were-all-wrong-sometimes-blindspots-biases-getting-better-w-dr-stephanie-nelson/
• “Reverb (AI-assisted report writing for psychologists)”: https://reverbreports.com/
• “Automation bias: a systematic review of frequency, effect, mediators, and mitigators (JAMIA, 2011)”: https://pmc.ncbi.nlm.nih.gov/articles/PMC3240751/
• “Humans and automation: use, misuse, disuse, abuse (Parasuraman & Riley, 1997)”: https://web.mit.edu/16.459/www/parasuraman.pdf
• “Automation-induced complacency: NASA review and experiments (Prinzel et al., 2001)”: https://ntrs.nasa.gov/api/citations/20020021642/downloads/20020021642.pdf
• “Cognitive forcing strategies in clinical decisionmaking (Croskerry, 2003)”: https://pubmed.ncbi.nlm.nih.gov/12514691/
• “Effect of availability bias and reflective reasoning on diagnostic accuracy (Mamede et al., JAMA 2010)”: https://jamanetwork.com/journals/jama/fullarticle/186585
• “The role of deliberate practice in the acquisition of expert performance (Ericsson, Krampe, & Tesch-Römer, 1993)”: https://cseweb.ucsd.edu/classes/fa08/cse599/Papers/Ericsson.pdf
• “Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI (Radiology, 2024)”: https://pubmed.ncbi.nlm.nih.gov/39560483/
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