Simulations constitute a fundamental component of medical and nursing education and traditionally employ standardized patients (SP) and high-fidelity manikins to develop clinical reasoning and communication skills. However, these methods require substantial resources, limiting accessibility and scalability. In this study, we introduce CLiVR, a Conversational Learning system in Virtual Reality that integrates large language models (LLMs), speech processing, and 3D avatars to simulate realistic doctor-patient interactions. Developed in Unity and deployed on the Meta Quest 3 platform, CLiVR enables trainees to engage in natural dialogue with virtual patients. Each simulation is dynamically generated from a syndrome-symptom database and enhanced with sentiment analysis to provide feedback on communication tone. Through an expert user study involving medical school faculty (n=13), we assessed usability, realism, and perceived educational impact. Results demonstrated strong user acceptance, high confidence in educational potential, and valuable feedback for improvement. CLiVR offers a scalable, immersive supplement to SP-based training.

翻译：模拟是医学与护理教育的核心组成部分，传统上采用标准化病人（SP）和高仿真人体模型来培养临床推理与沟通技能。然而，这些方法需要大量资源，限制了可及性与可扩展性。本研究提出CLiVR，一种集成大型语言模型（LLMs）、语音处理和三维虚拟形象的虚拟现实对话学习系统，用于模拟真实的医患交互。该系统基于Unity开发并部署于Meta Quest 3平台，使受训者能够与虚拟患者进行自然对话。每次模拟均基于症状-体征数据库动态生成，并通过情感分析增强以提供沟通语调的实时反馈。通过一项包含医学院教师（n=13）的专家用户研究，我们评估了系统的可用性、真实感及感知教育价值。研究结果显示用户接受度高，对其教育潜力具有强烈信心，并获得了宝贵的改进建议。CLiVR为基于标准化病人的培训提供了一种可扩展、沉浸式的补充方案。