Theory-informed redesign of clinical reasoning education in the era of generative artificial intelligence

Abstract Clinical reasoning is a foundational competency that develops over the course of training and comprises two sub-components. Diagnostic reasoning classifies a patient’s condition and assigns a diagnostic label, whereas management reasoning weighs multiple defensible plans with consideration of patient preferences and contextual constraints. Clinical reasoning education has historically emphasized “in-the-head” cognitive processes, such as information gathering, hypothesis generation, problem representation, and differential prioritization, over “out-in-the-world” contextualized processes, such as system navigation, use of tools, and interprofessional management. Generative artificial intelligence (GAI), itself a tool “out-in-the-world,” is changing where, with whom and what, and how trainees learn to reason. The unit of reasoning educators must attend to shifts from the individual learner to the learner-AI dyad. This shift creates opportunities for upskilling alongside risks of never-skilling, mis-skilling, and deskilling. Drawing on “in-the-head” information processing theories and “out-in-the-world” situativity theories, the authors propose a redesign of clinical reasoning education. They share learner vignettes to illustrate the upskilling potential and skilling risks most concerning at each stage of training before proposing five cross-cutting strategies for redesign: introduce GAI tools early in the curriculum, preserve reasoning-first GAI-assisted workflows, promote graduated autonomy for GAI use in clinical care, equip faculty to supervise GAI-assisted reasoning, and rethink clinical reasoning assessment. If GAI is integrated deliberately in a theory-informed way, the next generation of learners can be better prepared to incorporate GAI skillfully and responsibly into clinical reasoning.