The Application of Natural Language Processing in English Education

Natural language processing has quietly become a disruptive force in English education, promising personalized and efficient instruction at scale. This paper offers a systematic survey of how NLP is currently used in the field. The opening section examines Transformer-based models and their descendants, such as BERT, which now automate essay scoring, generate feedback tailored to individual students, and match reading materials to proficiency levels. The discussion then turns to earlier NLP techniques and evaluates where they still hold value. Qualitative and quantitative evidence for post-deployment outcomes is synthesized throughout the paper. Yet the review also highlights persistent obstacles: Technical limitations in processing languages from certain specific domain, or texts using rhetorical devices are still left unsolved. Ethical concerns about bias, privacy, and educational equality also cannot be ignored. By mapping both capabilities and shortcomings, the paper aims to inform educators, policymakers, and researchers who shape the next phase of NLP-enhanced learning.