# MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education

arXiv: https://arxiv.org/abs/2607.21570 · HTML: https://mlsanity.com/paper/2607.21570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [From Role-Play to Drama-Interaction: An LLM Solution](https://mlsanity.com/paper/2405.14231/) (2024)
- [InfiniteTalk: Audio-driven Video Generation for Sparse-Frame Video Dubbing](https://mlsanity.com/paper/2508.14033/) (2025)
- [LLMs Can Simulate Standardized Patients via Agent Coevolution](https://mlsanity.com/paper/2412.11716/) (2024)
- [PaniniQA: Enhancing Patient Education Through Interactive Question Answering](https://mlsanity.com/paper/2308.03253/) (2023)
- [StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning](https://mlsanity.com/paper/2405.13042/) (2024)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [Towards Enhanced Immersion and Agency for LLM-based Interactive Drama](https://mlsanity.com/paper/2502.17878/) (2025)
- [Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training](https://mlsanity.com/paper/2506.00386/) (2025)
- [DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge](https://mlsanity.com/paper/2509.07188/) (2025)
- [Game Development as Human-LLM Interaction](https://mlsanity.com/paper/2408.09386/) (2024)
- [Chatbot To Help Patients Understand Their Health](https://mlsanity.com/paper/2509.05818/) (2025)
- [PA-FAS: Towards Interpretable and Generalizable Multimodal Face Anti-Spoofing via Path-Augmented Reinforcement Learning](https://mlsanity.com/paper/2511.17927/) (2025)

## Abstract (arXiv)

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 textitMedGame, 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.