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Traditional grammar correction systems suffer from problems such as complex rules, sparse data, and insufficient utilization of contextual information. To address these issues, this article adopted Transformer\u2019s pre-trained language model, utilizing its powerful context understanding and automatic feature extraction capabilities to improve the processing performance of complex syntax structures and long-distance dependencies. Meanwhile, by constructing large-scale and diverse datasets and combining them with data augmentation techniques, the model\u2019s generalization ability and robustness were enhanced. This article also investigated hyperparameter tuning, model integration, and continuous optimization strategies in the process of model training optimization, and provided a detailed description of model evaluation and experimental validation. In the evaluation, the average precision, average recall, and average F1 score for most common grammar errors were 0.8, 0.805, and 0.801, respectively. The model in this article has excellent grammar correction ability. Through comprehensive experiments and evaluations, the potential and advantages of a new grammar correction artificial intelligence model developed based on computational linguistics methods in improving grammar correction effectiveness and practicality have been demonstrated.<\/jats:p>","DOI":"10.1177\/14727978251318799","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T13:17:51Z","timestamp":1738761471000},"page":"2992-3006","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Developing an artificial intelligence model for English grammar correction: A computational linguistics approach"],"prefix":"10.66113","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0517-6207","authenticated-orcid":false,"given":"Han","family":"Jiang","sequence":"first","affiliation":[{"name":"Basic Teaching Department, Hebi Automotive Engineering Professional College, Hebi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1515\/iral-2015-0062"},{"issue":"1","key":"e_1_3_3_3_2","first-page":"1","article-title":"Global Digital Compact: A Mechanism for the Governance of Online Discriminatory and Misleading Content Generation","volume":"2","author":"Zhi L","year":"2024","unstructured":"Zhi L, Wenyi Z, Hengtian Z, et al. 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