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Empowering Elementary Learning: Utilizing Large Language Models to Craft Tailored Textbooks with Expert Insight

Volume / Date

 VOL. 23 (NO. 2) / DEC.2025

Page

 pp.145-183

DOI

 10.6182/jlis.202512_23(2).145

Language

 English


Da-Chen Lian, Mao-Chang Ku, Po-Ya Angela Wang, Wei-Ling Chen, Shu-Kai Hsieh*
* Corresponding Author

Da-Chen Lian Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan
ORCID: 0009-0006-4850-4824
Mao-Chang Ku Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan
Po-Ya Angela Wang Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan
Wei-Ling Chen Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan
Shu-Kai Hsieh* Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan
shukaihsieh@ntu.edu.tw

Abstract

Large language models (LLMs) have in recent years spurred research across various sectors, owing to their remarkable zero-shot or few-shot performance. This capability has become indispensable for individuals seeking to integrate these language models into their workflows effectively. In this paper, based on in-depth linguistic analyses, we explore the application of an LLM, specifically GPT-4, in generating Chinese language textbooks tailored for grade school students. This encompasses the creation of main lesson texts alongside accompanying Chinese character exercises. Experimental results suggest that the LLM-generated textbook lessons are a viable research direction. The initial outcomes demonstrate the ability of LLM to generate texts of satisfactory quality appropriate for a specified grade level. The contributions of this work include pioneering the quantitative analysis of Chinese language textbooks for native speakers in Taiwan and leveraging an LLM to automatically generate textbook content and accompanying Chinese character exercises targeted at native Chinese speakers, which is a novel approach facilitated by the development of prompts tailored to different language learning levels. The study also conducts quantitative and qualitative comparisons between machine-generated lessons and those developed by educational professionals in Taiwan.

Keywords

Large Language Models; Chinese Language Education; Automatic Textbook Generation; Language Learning; In-context Learning