From Simulated Interaction to Intelligent Generation: A Generative Multi-Agent Virtual Patient Framework for Nursing Education with RAG-Based Safety Guardrails

Abstract

In response to the growing mismatch between nursing workforce demand and constrained clinical teaching resources, this study proposes a Generative Multi-Agent Virtual Patient (GMVP) framework for high-fidelity nursing education. Grounded in situated learning, cognitive apprenticeship, and distributed cognition, GMVP employs a triadic agent architecture comprising narrative, physiological, and evaluator agents to reconstruct social interaction, physiological coherence, and formative assessment in virtual clinical environments. A design-based research methodology guides iterative development and classroom deployment aligned with outcome-based education standards. To address hallucination risks in high-stakes medical content, the system integrates retrieval-augmented generation with modular validation and physiological consistency checks. The framework supports scalable case generation, learning analytics, and equitable access to complex clinical training scenarios.