MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education
For medical educators and students, MedGame offers a more engaging way to learn clinical decision-making, though it is an incremental application of LLMs to a known problem.
MedGame transforms static clinical cases into structured storytelling games for medical education, using a dual-engine LLM framework. Fine-tuning improves open-source LLMs on the 5,000-case benchmark, and a pilot study shows learners find it more engaging than text-only alternatives.
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.