用模糊推理系统实时评估医学生临床决策，提供精准反馈。
A Fuzzy Supervisor Agent Design for Clinical Reasoning Assistance in a Multi-Agent Educational Clinical Scenario Simulation
- 基于模糊推理系统分析学生与模拟患者互动
- 实时识别专业性、医学相关性等关键问题
- 适合医学教育仿真平台，提升教学反馈智能化
在临床情景训练中，协助医学生进行临床推理仍是医学教育中的长期挑战。本文提出并设计了多智能体医学教育情景模拟平台（MAECSS）中的新型组件——模糊督导代理（FSA）。FSA利用模糊推理系统（FIS），通过预设的模糊规则库，持续解析学生与患者、查体、诊断、干预等专业代理的交互行为，评估其专业性、医学相关性、伦理行为及情境干扰程度。通过实时分析学生的决策过程，FSA可提供自适应、上下文感知的反馈，并在学生遇到困难时精准介入。本研究聚焦FSA的技术架构与设计逻辑，强调其在基于仿真的医学教育中实现可扩展、灵活且类人化督导的潜力。未来工作将开展实证评估并推进其在更广泛教育场景中的集成。更详细的设计与实现已开源：https://github.com/2sigmaEdTech/MAS/
原文摘要 · Abstract (English)
Assisting medical students with clinical reasoning (CR) during clinical scenario training remains a persistent challenge in medical education. This paper presents the design and architecture of the Fuzzy Supervisor Agent (FSA), a novel component for the Multi-Agent Educational Clinical Scenario Simulation (MAECSS) platform. The FSA leverages a Fuzzy Inference System (FIS) to continuously interpret student interactions with specialized clinical agents (e.g., patient, physical exam, diagnostic, intervention) using pre-defined fuzzy rule bases for professionalism, medical relevance, ethical behavior, and contextual distraction. By analyzing student decision-making processes in real-time, the FSA is designed to deliver adaptive, context-aware feedback and provides assistance precisely when students encounter difficulties. This work focuses on the technical framework and rationale of the FSA, highlighting its potential to provide scalable, flexible, and human-like supervision in simulation-based medical education. Future work will include empirical evaluation and integration into broader educational settings. More detailed design and implementation is~\href{https://github.com/2sigmaEdTech/MAS/}{open sourced here}.
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