Integrated Natural Language Processing Algorithms for Automatic Feedback in English Writing: Improving Student Learning Outcomes Through Technology

Abstract

English writing is a core component of language learning and an important indicator of students’ comprehensive language application ability. Traditional writing instruction often faces heavy teacher correction workload, delayed feedback, inconsistent evaluation standards, and insufficient personalized guidance. This study integrates mainstream natural language processing algorithms, including text segmentation, part-of-speech tagging, grammatical error correction, semantic analysis, discourse-structure evaluation, and similarity-based scoring, to construct an automatic feedback model for English writing. A controlled experiment with non-English major college students is designed, with an experimental group using the automatic feedback system and a control group receiving traditional correction. Statistical analysis is conducted on grammar-error recognition, sentence optimization, discourse organization, lexical-collocation correction, writing-score changes, and learning autonomy. The results show that the integrated NLP feedback system can provide real-time and refined revision suggestions, shorten feedback cycles, standardize writing logic, and improve students’ writing proficiency and autonomous learning ability. In intelligent education environments, wireless learning terminals, cloudedge data transmission, and educational signal-processing infrastructure further support scalable deployment. The study provides a feasible path for technology-enhanced English writing instruction.