Author(s)

Fang Huang

Corresponding Author

Fang Huang

Abstract

The purpose of this paper is to deeply discuss the identification of language acquisition patterns based on AI (Artificial intelligence) and its application in teaching, so as to formulate and implement personalized teaching strategies and improve the effect of language teaching. Therefore, this paper first reviews the traditional research on language acquisition, and based on behaviorism and cognitive theory, divides language acquisition modes into two categories: natural acquisition and teaching acquisition, and further considers the individual differences of learners, learning environment and learning strategies. In terms of methods, this paper adopts a comprehensive feature analysis method to evaluate learners' language ability, learning motivation, learning style and cognitive style. Based on these analyses, we can set individualized teaching objectives for learners and choose suitable teaching contents and methods. The research shows that the identification of language acquisition mode based on AI and the formulation and implementation of personalized teaching strategies are of great significance for improving language teaching effect. Through comprehensive feature analysis, teachers can better understand learners, meet their unique needs and promote the all-round development of their language ability.

Keywords

Language acquisition model; Individualized teaching strategy; AI technology; Big data; Machine learning