{
  "id": 13476,
  "url": "https://arxiv.org/abs/2607.21570v1",
  "title": "MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education",
  "summary": "Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \\textit{MedGame}, a framework that transforms static clinical cases into structured, executable storytelling games. MedGame uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical stor",
  "authors": "Qian Wu, Xinrong Zhou, Zizhan Ma, Kai Chen, Zheyao Gao, Xun Lin, Hongqiu Wu, Longfei Gou, Yixiao Liu, Ann Sin Nga Lau, Qi Dou",
  "category": "research",
  "topics": "healthcare,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T17:50:28.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13476",
  "original_url": "https://arxiv.org/abs/2607.21570v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}