用人物角色设计可解释的医疗推理训练多智能体系统，提升学生临床思维能力。
Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Training
- 以人物角色驱动需求工程，融合用户故事捕捉多方利益相关者需求。
- 78%以上学生认为系统显著提升临床推理能力，验证了有效性。
- 适合教育AI、可解释性研究及医疗培训系统开发者参考。
随着人工智能和代理型AI在教育与医疗等领域的深入应用，确保多智能体教育系统（MAES）在需求工程阶段就具备可解释性至关重要。可解释性有助于建立信任、促进透明并实现人机高效协作。尽管人物角色在人机交互中已被广泛使用以代表用户并捕捉其需求与行为，但在可解释的MAES需求工程中的作用仍不充分。本文提出一种以人为本、以人物角色驱动的可解释MAES需求工程框架，并通过一个用于临床推理训练的MAES进行验证。该框架将人物角色与用户故事贯穿于整个需求工程过程，涵盖医学教育者、医学生、AI患者代理及临床代理（体格检查代理、诊断代理、临床干预代理、督导代理、评估代理）等多方利益相关者的需求、目标与交互。各代理的目标、底层模型与知识库共同塑造其交互行为，并指导可解释性要求，进而支撑医学生的临床推理训练。使用后调查结果显示，超过78%的医学生认为该系统提升了其临床推理能力。研究证明，基于人物角色的需求工程能有效连接技术需求与非技术用户，从早期阶段保障可解释的MAES具备可信性、可解释性，并与真实临床场景对齐。部分开源的临床情景模拟器可在GitHub获取。
原文摘要 · Abstract (English)
As Artificial Intelligence (AI) and Agentic AI become increasingly integrated across sectors such as education and healthcare, it is critical to ensure that Multi-Agent Education System (MAES) is explainable from the early stages of requirements engineering (RE) within the AI software development lifecycle. Explainability is essential to build trust, promote transparency, and enable effective human-AI collaboration. Although personas are well-established in human-computer interaction to represent users and capture their needs and behaviors, their role in RE for explainable MAES remains underexplored. This paper proposes a human-first, persona-driven, explainable MAES RE framework and demonstrates the framework through a MAES for clinical reasoning training. The framework integrates personas and user stories throughout the RE process to capture the needs, goals, and interactions of various stakeholders, including medical educators, medical students, AI patient agent, and clinical agents (physical exam agent, diagnostic agent, clinical intervention agent, supervisor agent, evaluation agent). The goals, underlying models, and knowledge base shape agent interactions and inform explainability requirements that guided the clinical reasoning training of medical students. A post-usage survey found that more than 78\% of medical students reported that MAES improved their clinical reasoning skills. These findings demonstrate that RE based on persona effectively connects technical requirements with non-technical medical students from a human-centered approach, ensuring that explainable MAES are trustworthy, interpretable, and aligned with authentic clinical scenarios from the early stages of the AI system engineering. The partial MAES for the clinical scenario simulator is~\href{https://github.com/2sigmaEdTech/MAS/}{open sourced here}.
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