Research on the Language Acquisition Model of Non-Native English Learners Based on Artificial Intelligence

Abstract

The widest application field of intelligent teaching supported by artificial intelligence technology is language learning, and enhancing learners’ language acquisition ability is the top priority. On the basis of analyzing the guidelines for the construction of a language acquisition model for non-native English learners, this paper combs through the main advantages of intelligent technology-assisted English teaching and establishes an intelligent language acquisition model based on artificial intelligence. Fuzzy cognitive diagnosis of the language acquisition level of non-native English learners is carried out using the Fuzzy-CDF model, the language acquisition ability of learners is modeled, and the regression model is constructed by combining with artificial intelligence technology so as to realize the bidirectional assessment of language acquisition level of learners. When the Fuzzy-CDF model is utilized for the learner’s language acquisition level, its MAE value is the lowest at 0.1385, and the influence coefficient of the teacher’s teaching method on the learner’s language acquisition ability is the largest at 23.506. The difference between the learner’s language acquisition response time at different levels on the experimental example sentences and the native learners is small, and close to 90% of the students indicated that they were more satisfied with the intelligent language acquisition model. Relying on artificial intelligence technology to construct intelligent language acquisition models can help non-native English learners improve their language acquisition ability.

Keywords

• artificial intelligence
• fuzzy-cdf model
• fuzzy cognitive diagnosis
• regression modeling
• language acquisition patterns
• 97p10