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}.

翻译：随着人工智能与Agentic AI 在教育和医疗等领域的广泛应用，从AI软件开发流程中需求工程早期阶段确保多智能体教育系统具备可解释性变得至关重要。可解释性是建立信任、促进透明度、实现高效人机协作的基础。尽管人物角色在人机交互领域已被广泛用于表征用户并捕捉其需求和行为，但其在可解释多智能体教育系统的需求工程中发挥的作用仍待深入探索。本文提出了一种以人为本、角色驱动的可解释多智能体教育系统需求工程框架，并通过一个面向临床推理训练的多智能体教育系统对该框架进行了演示。该框架在需求工程全流程中整合了人物角色与用户故事，用于捕捉多方利益相关者（包括医学教育者、医学生、AI患者代理、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））的需求、目标与交互。角色目标、底层模型与知识库共同塑造了代理间的交互行为，并定义了引导医学生临床推理训练的可解释性需求。后续使用调查显示，超过78%的医学生认为该多智能体教育系统提升了临床推理能力。研究结果表明，基于角色的需求工程能够从以人为中心的角度，有效连接技术需求与非技术背景的医学生，确保可解释多智能体教育系统从AI系统工程早期阶段即具备可信性、可解释性，并与真实临床场景保持一致。该临床场景模拟器的部分多智能体教育系统已开源（项目地址：\href{https://github.com/2sigmaEdTech/MAS/}{https://github.com/2sigmaEdTech/MAS/}）。