Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Training

2026-04-18

AI Summary

This study addresses the lack of explainability embedded from the requirements engineering phase in existing explainable multi-agent educational systems for clinical reasoning training, which hinders medical students’ trust in and effective collaboration with AI. To bridge this gap, the work proposes a human-centered, persona-driven requirements engineering framework that, for the first time, deeply integrates role-based personas and user stories of both medical educators and students into the full lifecycle design of an explainable multi-agent system—comprising patient, diagnostic, intervention, supervisory, and assessment agents—to achieve early alignment between technical requirements and non-technical user goals. Empirical evaluation demonstrates that over 78% of medical students reported significant improvements in clinical reasoning skills, thereby validating the proposed approach’s innovative contribution to enhancing both system trustworthiness and pedagogical efficacy.

Technology Category

• Multiagent Systems: Multiagent Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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}.

Problem

• Explainable AI
• Multi-Agent Education System
• Requirements Engineering
• Personas
• Clinical Reasoning

Innovation

• Persona-based Requirements Engineering
• Explainable Multi-Agent Systems
• Clinical Reasoning Training
• Human-Centered AI
• User Stories