CLiVR：搭载AI驱动患者的虚拟现实对话式学习系统
CLiVR: Conversational Learning System in Virtual Reality with AI-Powered Patients
AI总结：
针对传统医护模拟训练资源消耗大、难扩展的问题，研究推出整合大语言模型、语音处理与3D化身的VR对话学习系统CLiVR，专家研究验证其接受度高，可作为SP培训的沉浸式补充方案。
摘要：
模拟训练是医护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。但这类方法需要大量资源，限制了可及性与可扩展性。本研究推出CLiVR——一款虚拟现实对话式学习系统，它整合大语言模型（LLMs）、语音处理技术与3D虚拟化身，用于模拟真实的医患互动。该系统基于Unity开发，部署在Meta Quest 3平台上，可让受训者与虚拟患者开展自然对话。每次模拟场景均从综合征-症状数据库动态生成，并借助情感分析强化，以提供沟通语气方面的反馈。我们通过一项纳入医学院教职人员（n=13）的专家用户研究，评估了系统的可用性、真实感与感知教育价值。结果显示用户接受度高，对其教育潜力信心充足，同时也收集到了宝贵的改进建议。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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
