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586. Current Research on AI Adoption Among Psychologists w/ Dr. Adam Lockwood

Published 4 weeks ago
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In this episode, I sit down with researcher and consultant Dr. Adam Lockwood to explore the rapidly evolving integration of artificial intelligence within psychological assessment and practice. We dissect his empirical research evaluating the quality of AI-generated psychological reports compared to those written by licensed professionals, highlighting the surprising areas where technology outperforms human baseline standards, such as tailored intervention recommendations. Adam shares valuable data from his recent work detailing AI adoption rates across school psychology, health service psychology, and allied health fields, while addressing critical systemic challenges including automation bias, trainee de-skilling, and algorithmic bias. Our conversation offers a grounded, data-driven perspective on how practitioners can responsibly navigate these tools as intellectual thought partners rather than automated replacements.

Main Topics Discussed

  • 00:52: Conflict of interest disclosure and the core motivations behind transitioning from traditional assessment to empirical artificial intelligence research
  • 04:08: Early exploratory findings regarding the mechanical accuracy, APA formatting capability, and citation fabrications of early language models
  • 07:10: Comparing human and AI-generated clinical reports across various diagnostic conditions and evaluating user satisfaction metrics
  • 17:10: Surveying AI adoption rates and professional trends among health service psychologists and allied health professionals
  • 32:32: Navigating the specific risks of professional trainee de-skilling and the clinical pitfalls of automation bias
  • 45:38: Analyzing systemic bias, discrimination patterns, and treatment recommendation disparities in frontier models
  • 53:21: Evaluating the strengths, operational differences, and token allocation limits of major commercial language models
  • 58:40: Identifying the primary ethical, legal, accuracy, and privacy barriers preventing widespread AI adoption among practitioners

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