Generative AI in scenario-based healthcare education: A systematic review of applications, validation practices, and pedagogical integration

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The adoption of Generative AI (GenAI) and Large Language Models (LLMs) in healthcare education has accelerated rapidly since 2023, yet the evidence base for their use in Scenario-Based Learning (SBL) remains fragmented. This systematic review synthesises empirical research on GenAI applications across scenario-based, case-based, problem-based, and simulation-based learning in healthcare education. Following the PRISMA 2020 guidelines, five databases were searched on 9 November 2025 for peer-reviewed studies published from January 2023 onwards. Of 1151 initial records, 23 studies met the inclusion criteria. Quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Thematic synthesis identified six cross-cutting themes organised around a central finding: prompt design in educational contexts functions as a form of instructional specification, encoding the cognitive targets and quality criteria that would otherwise be implicit in expert authoring. Yet only 34.8% of studies aligned generated content with established instructional frameworks, and an equal proportion reported prompting strategies in sufficient detail for reproduction. Additional findings include: variable validation practices lacking standardisation; superior outcomes from hybrid human-AI collaboration over fully automated approaches; persistent evaluation gaps in longitudinal and comparative designs; and scalability as a primary adoption driver despite largely unquantified efficiency gains. GPT-4 dominated implementations (44.4%), while open-source alternatives were under-explored. Educational outcomes were generally positive for higher-order cognitive skills but inconsistent for knowledge acquisition. A four-stage validation pipeline is proposed as a conceptual framework to guide responsible deployment, pending empirical validation. These findings suggest that GenAI integration in healthcare SBL requires treating prompt design as a methodological element, standardising multi-stage validation, and formalising human-AI collaboration to realise its educational potential.