Advancements in large language models offer strong potential for enhancing virtual simulated patients (VSPs) in medical education by providing scalable alternatives to resource-intensive traditional methods. However, current VSPs often struggle with medical accuracy, consistent roleplaying, scenario generation for VSP use, and educationally structured feedback. We introduce an agentic framework for training general practitioner student skills that unifies (i) configurable, evidence-based vignette generation, (ii) controlled persona-driven patient dialogue with optional retrieval grounding, and (iii) standards-based assessment and feedback for both communication and clinical reasoning. We instantiate the framework in an interactive spoken consultation setting and evaluate it with medical students ($\mathbf{N{=}14}$). Participants reported realistic and vignette-faithful dialogue, appropriate difficulty calibration, a stable personality signal, and highly useful example-rich feedback, alongside excellent overall usability. These results support agentic separation of scenario control, interaction control, and standards-based assessment as a practical pattern for building dependable and pedagogically valuable VSP training tools.

翻译：大型语言模型的进步为增强医学教育中的虚拟模拟患者提供了巨大潜力，通过提供可扩展的替代方案来缓解传统方法资源密集的局限。然而，当前的虚拟模拟患者通常面临医学准确性不足、角色扮演一致性差、适用于虚拟模拟患者的场景生成困难以及缺乏教育结构化反馈等问题。本文提出了一种用于训练全科医学生技能的智能体框架，该框架整合了三个核心模块：(i) 可配置的、基于证据的临床情景生成，(ii) 可控的、基于人物设定的患者对话（支持可选的检索增强），以及 (iii) 基于标准的沟通与临床推理能力评估与反馈。我们将该框架实例化于一个交互式语音问诊环境中，并邀请医学生（$\mathbf{N{=}14}$）进行评估。参与者反馈称，对话具有真实性和情景忠实度，难度校准适当，人物性格信号稳定，反馈示例丰富且极具实用性，整体可用性优异。这些结果表明，将场景控制、交互控制与基于标准的评估进行智能体层面的分离，是构建可靠且具有教学价值的虚拟模拟患者训练工具的一种实用范式。