The Application of Artificial Intelligence in English Learning for Chinese Preschool Children: Based on the Zone of Proximal Development Theory

Abstract

The Zone of Proximal Development (ZPD) theory elucidates the gap between a learner’s actual developmental level—reflected in their ability to solve problems independently—and their potential developmental level—achieved when solving problems with guidance, providing a crucial theoretical framework for scaffolding instruction in second language acquisition for preschool children. Concurrently, the advent of artificial intelligence (AI) in education demonstrates transformative potential, particularly in facilitating personalized and adaptive learning experiences. This paper conducts a comprehensive analysis of AI applications in English learning for Chinese preschool children, grounded in the ZPD theory. Employing a combination of theoretical review and case analysis, the study addresses three core questions: the theoretical foundations and relevance of ZPD; the current state of AI implementation in this specific context; and practical pathways for integrating AI with ZPD theory. Findings indicate that while AI applications are emerging, they often lack a robust theoretical basis. Findings suggest AI can enhance language acquisition by providing dynamic assessment, personalized scaffolding, and developmentally appropriate challenges. This integration holds promise not only for improving linguistic competence but also for fostering cognitive and socio-emotional development, thus offering a novel intelligent paradigm for optimizing early childhood English education in China.