JMIR Publications

ABSTRACT

The increasing use of generative Large Language Models (LLMs) necessitates a fundamental reevaluation of traditional didactic lectures in medical education, particularly within psychiatry. The specialty's inherent diagnostic ambiguity, biopsychosocial complexity, and reliance on nuanced interpersonal skills demand an educational model that transcends mere information transfer, focusing instead on cultivating sophisticated clinical reasoning. This commentary argues for a shift from passive knowledge transmission to the active, facilitated development of higher-order thinking, aligning with Bloom's Taxonomy. We propose four core propositions: 1) Shifting foundational knowledge acquisition to faculty-curated, asynchronous AI-assisted micro-modules. 2) Transforming synchronous time into "Ambiguity Seminars" for discussing nuanced cases, biopsychosocial formulation, and ethical dilemmas, leveraging faculty expertise in guiding reasoning. 3) Integrating live LLM critical interaction drills to develop prompt engineering skills and critical appraisal of AI outputs. 4) Realigning assessment methods (e.g., OSCEs, reflective writing) to evaluate clinical reasoning and integrative skills rather than rote recall. Successful implementation requires robust faculty development and resource allocation. This reimagined approach aims to cultivate clinical wisdom equipping psychiatric trainees with adaptive reasoning frameworks essential for excellence in an AI-mediated future.