The inpatient ward is a cornerstone of medical training but is increasingly strained by high levels of trainee burnout and withering educational engagement. The Job Demands-Resources (JD-R) model offers a useful framework for understanding these challenges. The model categorizes aspects of work as either demands or resources and describes how mismatches between the two can lead to exhaustion and impaired performance, while the right balance (high demands with commensurate resources) promotes work engagement and professional growth. Although widely used in other work settings, the JD-R model has seen limited application in medical education. This perspective describes the JD-R model and illustrates how it can be applied to evaluate educational practices on the inpatient wards. These practices can function either as demands or as resources depending on how they are implemented. A day-in-the-life vignette illustrates the application of the JD-R model, highlighting opportunities for attending physicians and program leaders to shape the work environment in ways that promote demand-resource balance and foster trainee engagement and learning on the inpatient wards.

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.