Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LLMs) in legal education to help non-experts learn intricate legal concepts through storytelling, an effective pedagogical tool in conveying complex and abstract concepts. We also introduce a new dataset LegalStories, which consists of 294 complex legal doctrines, each accompanied by a story and a set of multiple-choice questions generated by LLMs. To construct the dataset, we experiment with various LLMs to generate legal stories explaining these concepts. Furthermore, we use an expert-in-the-loop approach to iteratively design multiple-choice questions. Then, we evaluate the effectiveness of storytelling with LLMs through randomized controlled trials (RCTs) with legal novices on 10 samples from the dataset. We find that LLM-generated stories enhance comprehension of legal concepts and interest in law among non-native speakers compared to only definitions. Moreover, stories consistently help participants relate legal concepts to their lives. Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment. Our work has strong implications for using LLMs in promoting teaching and learning in the legal field and beyond.

翻译：向非专业人士普及法律知识对于提升公众法律素养和促进民主公民参与至关重要。然而，对于缺乏法律背景的人士而言，法律文件通常难以理解。本文提出一种大型语言模型在法学教育中的创新应用，通过叙事——一种传授复杂抽象概念的有效教学工具——帮助非专业人士学习精深的法律概念。我们同时引入新数据集LegalStories，该数据集包含294条复杂法律原则，每条均配有由大型语言模型生成的叙事文本及配套选择题集。在数据集构建过程中，我们尝试使用多种大型语言模型生成解释这些概念的法律叙事。此外，我们采用专家参与循环方法迭代设计选择题。随后，我们通过随机对照试验，利用数据集中10个样本对法律初学者进行测试，评估大型语言模型叙事教学的有效性。研究发现：相较于仅提供定义，大型语言模型生成的叙事能显著提升非母语者对法律概念的理解程度及对法律的兴趣；叙事持续帮助参与者将法律概念与自身生活经验建立联系；后续评估显示，采用叙事学习的非母语者知识留存率更高。本研究对大型语言模型在法学及其他领域促进教学实践具有重要启示。