Journal Paper Published

Abstract

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.

Keywords

AI-assist learning, Chinese as a foreign language, Large language models, prompt engineering, scaffolding theory of learning, zone of proximal development