LitLinker: Supporting the Ideation of Interdisciplinary Contexts with Large Language Models for Teaching Literature in Elementary Schools

要旨

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.

著者
Haoxiang Fan
Sun Yat-sen University, Guangzhou, China
Changshuang Zhou
University of Macau, Macau SAR, China
Hao Yu
Sun Yat-sen University, Zhu Hai, Guang Dong, China
Xueyang Wu
NeurlStar, Shenzhen, Guangdong, China
Jiangyu Gu
Xiangzhou Experimental School of Zhuhai, Zhuhai, China
Zhenhui Peng
Sun Yat-sen University, Zhuhai, Guangdong Province, China
DOI

10.1145/3706598.3714111

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714111

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: AI in the Classroom

G303
6 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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