In recent years, advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly changed the landscape of education. Among the most promising developments is the emergence of chatbot-based language tutors, which leverage AI to offer personalized and interactive language learning experiences. These tutors can assist learners in mastering vocabulary, grammar, pronunciation, and conversation skills across various languages. This research examines the role of chatbot-based language tutors utilizing deep learning (DL) to facilitate English language acquisition in mobile applications. Intent categorization is a fundamental component of these systems, allowing chatbots to understand user questions and respond appropriately. To address related issues, the research created a proofreading chatbot designed to help academic authors with grammatical corrections. Data was collected from a publicly available chatbot-based English learning dataset. The data was preprocessed using stop word removal and tokenization. Term Frequency Inverse Document Frequency (TF-IDF) is utilized to extract features from the preprocessed data. Efficient pigeon inspired fused bidirectional long short-term memory (EPI-BiLSTM) is applied to classify the intent based on the text to determine the user’s intent. After the classification, to address data scarcity in grammatical error correction for the English language, back translation is employed as a data augmentation tool. Back translation involves translating error-prone sentences into a different language and then translating them back to the original language, generating parallel corpora with their corrected counterparts, derived from texts. The experimental results demonstrated that EPI-BiLSTM outperforms traditional algorithms based on domain (80.5%), intent (90.3%), entity (75.2%), and average accuracy (81.3%). These findings illustrate the potential of combining chatbot-based systems and DL techniques to address both proofreading and grammatical error correction challenges in mobile applications.