Auxiliary Teaching System for Personalized College English Learning based on Bidirectional Encoder Representations from Transformers Model
Ying Liu · 2025
College English education in most schools tends to fail to address students' individual learning requirements. Auxiliary teaching practices are mostly based on rule-based systems or superficial machine learning models without the capacity for contextual understanding, which restricts their performance in tasks like grammar correction, reading comprehension, vocabulary improvement, and sentence analysis. Consequently, students are provided with generalized feedback, which might fail to facilitate their language growth or promote active learning. In response to these shortfalls, the present research offers a deep learning-based College English Auxiliary Teaching System based on BERT (Bidirectional Encoder Representations from Transformers) as the fundamental model. BERT enjoys a reputation for its capability in contextual understanding of language by being bidirectionally trained, making it particularly ideal for many natural language processing tasks critical to the learning of English. The system under consideration is a multi-module system with various functional modules such as grammar correction, comprehension support, vocabulary recommendation, and sentence feedback. Each module is aimed at engaging with learners by processing their input and giving intelligent and adaptive feedback in real time. By employing the deep contextual understanding of BERT, the system provides customized learning experiences that accommodate individual students' needs. Experimental results indicate that the BERT-based system greatly enhances the feedback's accuracy, relevance, and utility over conventional methods. This research indicates the great promise of transformer-based models in English language learning.