| Sumario: | Health-professions education has moved beyond traditional lecture-based instruction toward competency-based and student-centred learning approaches, such as Problem-Based and Case-Based Learning. While pedagogy has evolved, the structured integration of artificial intelligence (AI) into medical and healthprofessions education remains limited. Current curricula provide minimal exposure to AI fundamentals, data literacy, and ethics, leaving future clinicians underprepared for AI-enabled practice. This paper extends the Learning, Cognition, AI, and Pedagogy (L-CAP) framework, originally introduced in a continuing-professionaldevelopment programme for educators, to health-professions education. Grounded in cognitive science, pedagogy, and software-architecture principles, L-CAP provides a structured, human-centred model for embedding AI through four interdependent layers: Learning, Cognition, AI, and Pedagogy, linked by a Plan- Orchestrate-Assess-Reflect workflow. The framework supports applications in clinical reasoning, inter-professional learning, and ethics education. Early exploratory feedback from the United Kingdom and South Korea suggests that LCAP is clear, adaptable, and suitable for integration across medical and allied-health curricula. L-CAP thus offers a practical bridge between pedagogy and technology, supporting more integrated, AI-informed, and cognitively grounded healthprofessions education.
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