Aug 2026· Journal of College of Education· Vol 64, pp. 683-698· 0 citations
TL;DR
The study comes to the conclusion that AI is a powerful instrument for confirming frequency-based linguistic theory but does not model the human cognitive journey.
Abstract
This study investigates grammatical trends in texts generated by artificial intelligence and human learners. The study puts to the test a fundamental principle of usage-based grammar: language is learned through repeated exposure to patterns. A direct comparison is conducted between AI-generated writings and language learners' essays. Quantitative approaches count words, sentences, and grammatical errors. Qualitative analysis detects trends in sentence structure and specific qualities such as past tense. Finding out if AI models adhere to usage-based grammar rules is the aim. Comparing the two groups' mistake types is another objective. The results show that whereas human writing varies, AI output is very constant. Almost no grammatical errors were found in AI articles, according to the study. Expected errors in human texts include omissions and overgeneralizations. The findings also demonstrate that AI makes greater use of components like the past tense and plurals. These studies demonstrate that the outcomes of usage-based learning are operationally replicated by AI. The results of training the model on massive amounts of data are consistent and precise. The ongoing process of language acquisition is reflected in human output. The study comes to the conclusion that AI is a powerful instrument for confirming frequency-based linguistic theory.but does not model the human cognitive journey. Future research should investigate different AI models and learner proficiency levels
The findings show that AI-generated texts exhibit greater lexical diversity and syntactic complexity; however, they often exhibit structural uniformity, overuse of cohesive devices, and limited pragmatic depth, and should not replace professionally designed educational materials.
V. Smaglii, T. Korolova, Svitlana Yukhymets et al.· Arab World English Journal· 0 citations
Analysis of AIGC texts points out that the complexity of AI text primarily stems from its mechanism of selecting vocabulary based on probability distributions, which favors longer words, abstract nouns, and words with high semantic content, thereby forming a highly compact linguistic surface.
Lulu Chen· Lecture Notes on Language an...· 0 citations
This study examined the use of artificial intelligence (AI) in facilitating the examination of student writing errors through the Systemic Functional Linguistics (SFL) framework. The objective was to create an AI-assisted assessment tool proficient in detecting faults associated with ideational, interpersonal, and linguistic metafunctions in writing. This method was compared to conventional error analysis techniques, emphasizing grammar, vocabulary, and punctuation. A mixed-methods strategy was utilized. A qualitative study investigated the categories and origins of errors, whereas a quantitative analysis documented the prevalence and distribution of these errors. The dataset consisted of 50 argumentative essays written by Pakistani university students studying English as a second language. Each essay varied in length from 200 to 400 words. Errors were initially detected utilizing OpenAI tools (GPT-4o mini model) and subsequently validated manually by SFL expert for precision. A total of 475 errors were detected: 194 (41%) interpersonal errors, 240 (51%) textual errors, and 41 (8%) ideational errors. The findings indicate that SFL facilitates a profound comprehension of writing challenges, transcending superficial faults to uncover students' conceptualization and organization of their ideas. The SFL methodology revealed more profound insights into student cognition and expression than traditional methodologies. This work emphasizes AI's capacity to integrate conventional and functional methods in error analysis. It proposes practical applications for instructors seeking to provide more substantive, formative feedback to enhance students' writing abilities.
Pari Abdul Aziz, Noshaba Bano, Muhammad Asim Mehmood et al.· Journal of innovative resear...· 0 citations
The results indicate that the complexity of syntax is genre-based and not source-based and in general, the discipline genre had a more significant effect on syntax variation than the authorship source.
Asia A. Alheety, Meethaq Khamees Khalaf, H. Mohammed· Arab World English Journal· 0 citations
Writing remains one of the most challenging skills for English as a Second Language (ESL) learners because it requires the coordinated application of grammar, vocabulary, and written discourse conventions. This study proposes an Intelligent Writing Tutor that integrates corpus-informed error analysis, natural language processing (NLP), and rule-based reasoning to generate individualized and explainable writing feedback for ESL learners. Guided by a mixed-methods Design Science Research approach, weekly journal entries produced by Ilocano-speaking English language majors at the Kalinga State University served as the learner corpus for analysis. Manual expert annotation based on Corder's Error Analysis framework identified lexical, morphological, syntactic, and mechanical errors that informed the development of an interpretable rule-based feedback engine. The analysis revealed that morphological errors were the most frequent, followed by mechanical, lexical, and syntactic errors, with verb tense misuse emerging as the dominant writing difficulty. Qualitative findings further indicated that many of the observed errors reflected first-language interference, particularly in tense marking, subject–verb agreement, preposition usage, and lexical choice. The proposed framework operationalizes learner texts through preprocessing, NLP-assisted error detection, rule-based error classification, feedback generation, and recommendation modules to produce individualized feedback reports. The study demonstrates the feasibility of integrating explainable NLP techniques and second language acquisition principles into an educational writing support system. Because the framework was developed using journal writings from Ilocano-speaking learners in a single institution, its applicability to other learner populations requires further investigation.
R. L. Ladwingon· International Journal of Adv...· 0 citations
There is a conclusion that although AI has become an inseparable part of linguistics today, there is a need to establish a balanced approach of using computational methods and knowledge of human linguists to be sustainable and ethical.
Silvia Diallo, Chinedu Eze· International Journal of Inn...· 0 citations
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