Natural language processing in language learning: personalized and adaptive English language teaching using artificial intelligence

Abstract

In recent years, artificial intelligence technology has begun to be widely used in the field of language teaching in colleges and universities. As an important branch of artificial intelligence, the study introduces natural language processing technology into English teaching in colleges and universities, utilizes it to extract rich semantic features in English teaching resources, combines LSTM and attention mechanism, and designs an English teaching resource recommendation model based on student's interests. Based on the recommendation model, an adaptive platform for English teaching is constructed. A small-scale trial is conducted to discuss students' feedback after the trial and explore the application effect of the platform. The results of each index of the English teaching resources recommendation model are optimal, and the recall (88.65%), accuracy (90.29%) and NGDD (0.3725) are higher than those of other models, which proves the model's effectiveness in resource recommendation. The positive feedback from students on the adaptive effect, applicability, and recognition of the adaptive platform for English teaching is basically above 50%. The analysis reveals that the AI-based resource recommendation model and adaptive English teaching can aid English teaching in colleges and universities and enhance the quality of English teaching.