Research on English Learning System of English Chatbot Based on Deep Learning

The rapid growth of artificial intelligence and natural language processing has led to the development of intelligent chatbots that can assist in language learning, providing personalized and interactive support to learners. However, many existing English learning systems face limitations such as poor contextual understanding, inability to handle complex sentence structures, and lack of adaptability to individual learner proficiency levels. To address these challenges, this research proposes an English learning chatbot based on the Bidirectional Encoder Representations from Transformers (BERT) model, which leverages deep learning to understand language context more effectively. BERT's bidirectional attention mechanism enables the chatbot to process both preceding and succeeding words in a sentence, allowing it to deliver contextually accurate responses and explanations. For implementation, datasets such as the Cornell Movie Dialogues Corpus and DailyDialog are utilized to train the model for diverse conversational scenarios, while the English Learning Corpus (ELC) is incorporated to adapt the chatbot to educational contexts. The system is designed to provide grammar corrections, vocabulary suggestions, and conversational practice in real time. Experimental results demonstrate that the BERT-based chatbot achieves superior accuracy in intent recognition and contextual response generation compared to traditional RNN, with a 15–20% improvement in response relevance and grammatical correctness. These results indicate that the proposed model can significantly enhance English learning efficiency by offering interactive, intelligent, and context-aware conversations.