一种用于培训全科医学生技能的代理AI框架
An Agentic AI Framework for Training General Practitioner Student Skills
AI总结：
本文提出了一种代理AI框架，用于培训全科医学生技能，通过统一病例生成、角色驱动对话和基于标准的评估，提升医学教育中虚拟模拟患者的效果。
摘要：
大型语言模型的进步为医学教育中的虚拟模拟患者（VSP）提供了强大的潜力，通过提供可扩展的替代方案来弥补资源密集型传统方法的不足。然而，当前的VSP往往在医学准确性、一致的角色扮演、VSP使用的场景生成和教育结构化的反馈方面存在困难。我们介绍了一种用于培训全科医学生技能的代理框架，该框架统一了（i）可配置的、基于证据的病例生成（ii）受控的以角色驱动的患者对话，带有可选的检索基础，以及（iii）基于标准的评估和反馈，用于沟通和临床推理。我们在一个互动的口头咨询环境中实现了该框架，并通过14名医学生进行了评估。参与者报告了真实且符合病例的对话，适当的难度校准，稳定的个性信号，以及高度有用的例子丰富的反馈，同时整体可用性也非常好。这些结果支持了将场景控制、交互控制和基于标准的评估分离作为构建可靠且具有教学价值的VSP训练工具的实用模式。
英文摘要：
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.
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。
