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JTCLT Abstract

Volume 15 Number 1, 2024
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Zhao, Q., Hsu, Y. Y., & & Huang, C. R. (2024). Large Language Model and Chinese Near Synonyms: Designing Prompts for Online CFL Learners. Journal of Technology and Chinese Language Teaching, 15(1), 49-69.
[肇群, 許又尹, & 黃居仁. (2024). 大语言模型与汉语近义词:针对二语学习者线上学习的提示设计. 科技与中文教学 (Journal of Technology and Chinese Language Teaching), 15(1), 49-69.]

Full paper


We propose a novel approach of applying large language models (LLMs) to better identify the Zone of Proximal Development (ZPD) of learners of Chinese as a foreign language (CFL). In particular, we designed prompts that assist LLMs in identifying the correct ZPD for CFL learners in order to provide more effective scaffolding. This study utilizes near synonyms to actuate this scaffolding procedure. By beginning with a base prompt and optimizing it in iterative instances, the models are better able to identify proper use-cases for the nuances of each near synonym, leading to more accurate and practical feedback responses. In three experiments, we used different prompts to test the capability of LLMs to understanding and differentiating near synonyms. We found that prompts containing explanations and guidance of reasoning can significantly improve the performance of these models. We attribute this improvement to the addition of interactive learning in prompt design. Adopting the scaffolding framework of learning, we propose the “Zone of Proximal Development Prompts” that can help LLMs to properly identify the correct ZPD of the CFL learners.


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