用大模型让虚拟病人随护士对话水平自动调整反应，提升培训真实感。
Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training
- 基于大模型动态分析护士对话，实时调节虚拟病人行为。
- 在模拟场景中表现与真实护士沟通水平一致，评估准确。
- 适合护理培训、医学教育者及智能医疗系统研发者使用。
有效的沟通训练对培养高质量护理人才至关重要。虽然标准化病人（SP）模拟提供宝贵经验学习，但成本高且灵活性差。虚拟病人（VP）系统虽具可扩展性，但多数无法根据学员沟通能力动态调整。当学员回应无效时，虚拟病人应表现出更强敌意或不合作——这一自适应交互仍鲜有支持。为此，我们提出 Adaptive-VP，一个基于大语言模型（LLMs）的虚拟病人对话生成框架，可根据学员输入动态调整虚拟病人行为。该框架包含构建临床合理且灵活的虚拟病人场景的流程，以及模块化系统，用于实时评估学员沟通能力并调整虚拟病人响应，同时保障学习者安全。通过模拟具有挑战性的患者对话进行验证，自动化评估显示我们的沟通技能评估机制与执业护士语料库中的实际水平相符。专家护士进一步确认，Adaptive-VP 产生的互动比现有方法更自然、更真实，展现出其作为可扩展、高效护理沟通培训工具的潜力。
原文摘要 · Abstract (English)
Effective communication training is essential to preparing nurses for high-quality patient care. While standardized patient (SP) simulations provide valuable experiential learning, they are often costly and inflexible. Virtual patient (VP) systems offer a scalable alternative, but most fail to adapt to the varying communication skills of trainees. In particular, when trainees respond ineffectively, VPs should escalate in hostility or become uncooperative--yet this level of adaptive interaction remains largely unsupported. To address this gap, we introduce Adaptive-VP, a VP dialogue generation framework that leverages large language models (LLMs) to dynamically adapt VP behavior based on trainee input. The framework features a pipeline for constructing clinically grounded yet flexible VP scenarios and a modular system for assessing trainee communication and adjusting VP responses in real time, while ensuring learner safety. We validated Adaptive-VP by simulating challenging patient conversations. Automated evaluation using a corpus from practicing nurses showed that our communication skill evaluation mechanism reflected real-world proficiency levels. Expert nurses further confirmed that Adaptive-VP produced more natural and realistic interactions than existing approaches, demonstrating its potential as a scalable and effective tool for nursing communication training.
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