发表机构
CUHK; Southern Medical University; Peking University; Tencent（香港中文大学; 南方医科大学; 北京大学; 腾讯）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在利用大语言模型助力医学教育，提出MedGame框架，通过双引擎设计将临床病例转化为故事化游戏，构建MedGame Bench进行评估，实验显示特定任务微调提升开源模型表现，学生研究表明其更具吸引力和实用性。
AI 中文摘要
大语言模型在医学教育中展现出潜力，但现有多数系统专注于局部交互，而非将整个临床病例组织成以决策为中心的学习轨迹。我们引入MedGame，一个将静态临床病例转化为结构化、可执行的故事化游戏的框架。MedGame采用双引擎设计，医学叙事设计师合成基于病例的临床故事情节，故事导演将其转化为依赖感知的多模态编排计划。我们构建了MedGame Bench作为医学叙事生成和故事指导的基准和评估协议。实验表明特定任务微调显著提升了开源大语言模型在MedGame Bench上的表现并缩小与商业模型的差距。一项学生试点研究进一步表明学习者认为MedGame比纯文本替代方案更具吸引力和实用性。
英文摘要
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 storylines with states and decision nodes, while a Story Director converts them into dependency-aware multimodal orchestration plans rendered by our released interactive platform. We construct MedGame Bench, a 5,000-case benchmark and evaluation protocol for Medical Narrative Generation and Story Direction. Experiments show that task-specific fine-tuning substantially improves open-source LLMs on MedGame Bench and narrows the gap with commercial models. A pilot student study further shows that learners perceive MedGame as more engaging and useful than text-only alternatives.
CommentsWork in Progress; an explorational design and study on AI+Education+Game
