用大模型玩实体游戏，让孩子和AI一起写故事。
Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models
- 双模型迭代：一个写故事，一个评反馈，反复优化。
- 仅需少量修改，生成故事质量显著提升。
- 适合教育场景，尤其儿童互动创作研究者。
协同创作——即人工智能代理与人类共同生成输出（如艺术作品）——近年来备受关注。然而，现有研究多聚焦于成人与数字环境中的交互。本文探索了一种新颖的游戏化协同创作场景：儿童通过实体棋盘游戏与大型语言模型（LLMs）协作生成书面故事。目标是构建一个能生成适合年幼儿童的高质量叙事的多智能体框架。核心方法为迭代式‘作者-编辑’流程：一个LLM生成故事，另一个评估并提供改进建议。通过多模型模拟实验发现，这种迭代交互在连续循环中持续提升故事的感知质量。结果表明，少量优化步骤即可在互动叙事系统中实现高质量输出。
原文摘要 · Abstract (English)
The topic of Co-creation, i.e., AI agents interacting with humans to generate outputs (e.g., art), has gained significant attention recently. However, most studies focus on adult-human interactions in a digital setting. This paper explores a novel ludic co-creation scenario involving children and Large Language Models (LLMs) interacting through a physical board game to create written stories. Our goal is to develop a multi-agent framework capable of producing high-quality narratives suitable for young players. At the core of our approach is an iterative Writer-Editor process in which one LLM generates stories while another evaluates them and provides feedback for refinement. Through a simulation study involving multiple LLMs, we show that this iterative interaction consistently improves the perceived quality of generated stories across successive loops. The results indicate that a small number of refinement steps may be sufficient to achieve high-quality outputs in interactive storytelling systems.
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