用大模型让虚拟病人有性格，提升医患沟通训练真实感
When Avatars Have Personality: Effects on Engagement and Communication in Immersive Medical Training
- 用模块化架构分离人格与病情数据，实现一致性格的虚拟患者
- 医生反馈该系统显著提升训练沉浸感和学习价值
- 适合需要提升临床沟通能力的医疗培训人员
虽然虚拟现实（VR）擅长模拟物理环境，但在训练复杂人际技能方面受限于虚拟人类的心理合理性不足。这一差距在医学教育中尤为关键，因沟通是核心临床能力。本文提出一个框架，将大语言模型（LLMs）融入沉浸式VR，创建具有独特且一致人格、符合医学逻辑的虚拟患者，基于解耦人格与临床数据的模块化架构。我们在包含持证医师的混合方法、自身对照研究中评估了该系统，进行模拟问诊。结果表明该方法可行，且被评价为富有成效且令人满意的训练增强。分析揭示关键设计原则，包括‘真实感-语量悖论’，以及挑战需被感知为临床上真实才能有效支持学习。
原文摘要 · Abstract (English)
While virtual reality (VR) excels at simulating physical environments, its effectiveness for training complex interpersonal skills is limited by a lack of psychologically plausible virtual humans. This gap is particularly critical in medical education, where communication is a core clinical competency. This paper introduces a framework that integrates large language models (LLMs) into immersive VR to create medically coherent virtual patients with distinct, consistent personalities, based on a modular architecture that decouples personality from clinical data. We evaluated the system in a mixed-methods, within-subjects study with licensed physicians conducting simulated consultations. Results suggest that the approach is feasible and perceived as a rewarding and effective training enhancement. Our analysis highlights key design principles, including a "realism-verbosity paradox" and the importance of challenges being perceived as clinically authentic to support learning.
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