{
  "id": 5681,
  "url": "https://arxiv.org/abs/2604.17186v1",
  "title": "Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Training",
  "summary": "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 represen",
  "authors": "Weibing Zheng, Laurah Turner, Jess Kropczynski, Matthew Kelleher, Murat Ozer, Shane Halse",
  "category": "research",
  "topics": "healthcare,children-education,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-19T01:18:44.000Z",
  "fetched_at": "2026-07-14T16:31:57.533Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5681",
  "original_url": "https://arxiv.org/abs/2604.17186v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}