用大模型辅助小学语文教学设计跨学科情境，提升效率与深度。
LitLinker: Supporting the Ideation of Interdisciplinary Contexts with Large Language Models for Teaching Literature in Elementary Schools
- 基于大模型生成跨学科主题并关联文本细节
- 实测显示比普通聊天机器人提升学科融合深度
- 适合一线教师快速构思跨学科教学方案
在小学语文教学中，将阅读材料与科学、艺术等跨学科内容结合已成趋势。但教师需大量查阅资料并建立关联，难度较高。本文通过与13位教师的迭代设计，开发了LitLinker系统，利用大语言模型推荐跨学科主题，并将其与阅读材料中的文学元素（如段落、观点）进行关联。一项包含16名参与者的对照实验表明，相比普通大模型聊天机器人，LitLinker能显著提升学科融合深度，减轻教师工作负担。9位专家访谈进一步验证其在跨学科教学构思中的实用性。研究最后提出使用大模型支持跨学科教学的设计反思与注意事项。
原文摘要 · Abstract (English)
Teaching literature under interdisciplinary contexts (e.g., science, art) that connect reading materials has become popular in elementary schools. However, constructing such contexts is challenging as it requires teachers to explore substantial amounts of interdisciplinary content and link it to the reading materials. In this paper, we develop LitLinker via an iterative design process involving 13 teachers to facilitate the ideation of interdisciplinary contexts for teaching literature. Powered by a large language model (LLM), LitLinker can recommend interdisciplinary topics and contextualize them with the literary elements (e.g., paragraphs, viewpoints) in the reading materials. A within-subjects study (N=16) shows that compared to an LLM chatbot, LitLinker can improve the integration depth of different subjects and reduce workload in this ideation task. Expert interviews (N=9) also demonstrate LitLinker's usefulness for supporting the ideation of interdisciplinary contexts for teaching literature. We conclude with concerns and design considerations for supporting interdisciplinary teaching with LLMs.
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