Jul 2026· Arab World English Journal· 0 citations· 15 references
TL;DR
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.
Abstract
The rapid integration of artificial intelligence (AI) into education has significantly changed how we approach language learning and teaching. Even though AI-generated materials are becoming more common in English language instruction, there is still not enough linguistic research comparing these texts with those designed for textbooks. This study aims to fill that gap by comparing human-authored and AI-generated texts used in language learning. The goal is to identify the main linguistic differences between these two types of texts and assess their effectiveness as language-learning models. The study uses texts from High Note and Language Leader alongside those generated by ChatGPT on similar topics, all within the B1–B2 proficiency range. The analysis examines lexical richness, syntactic complexity, cohesion, and discourse organization using both quantitative and qualitative methods. 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. In contrast, textbook-based texts demonstrate balanced grammar, controlled vocabulary selection, greater communicative authenticity, and a stronger fit with teaching goals. The study emphasizes the need to balance technological innovation with effective teaching in language education. The results suggest that while AI-generated texts can be helpful as supplementary resources, they should not replace professionally designed educational materials. This research contributes to ongoing discussions on artificial intelligence in English language teaching and offers insights into the linguistic and educational impacts of AI-assisted learning resources
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
: Based on a corpus-based methodology, this study analyzes the intrinsic reasons for the high level of lexical complexity observed in Artificial Intelligence Generated Content (AIGC). The research compares 24 English argumentative essays written by AI with 24 second-language (L2) learner essays reaching the IELTS Writing Task 2 Band 7 level. Under controlled conditions of identical genre and topic, the study performs quantitative statistics across three dimensions: lexical sophistication, semantic abstraction, and information density. Statistical results indicate that the frequency of advanced vocabulary in AI texts is significantly higher, approximately 2.3 times that of human texts. The proportion of abstract nouns reached 9.14%, far exceeding the 3.32% found in human texts, suggesting that AI expressions tend toward nominalization and conceptualization. Regarding overall information organization, the lexical density of AI texts was 69.9%, also surpassing the 60.3% of human texts, reflecting a stronger tendency for information condensation and phrasal structures. The analysis points out that the complexity of AI text primarily stems from its mechanism of selecting vocabulary based on probability distributions. This mechanism favors longer words, abstract nouns, and words with high semantic content, thereby forming a highly compact linguistic surface. Such complexity is essentially a formal feature at the statistical level and is not entirely equivalent to the proficiency levels corresponding to human L2 acquisition. These findings provide empirical references for AI text identification, the refinement of writing evaluation standards, and L2 writing pedagogy.
Lulu Chen· Lecture Notes on Language an...· 0 citations
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
Assis. lect. Batool Abdul-Mohsin Miri· Journal of College of Educat...· 0 citations
It is suggested that although AI tools provide sufficient comprehensible input, they do not consistently provide language at an optimal level as proposed by Krashen’s input hypothesis (i+1).
Nooe Fatima Lashari, Guo Fengmeng, Ayaz Ahmed Maganhar· International "Journal of Ac...· 0 citations
The significance of this study is that it uses Intelligent Computer-Assisted Language Learning as an interpretive lens to make sense of a rapidly shifting field, offering a framework to help educators navigate modern generative tools.
Ese Emmanuel Uwosomah· Arab World English Journal· 1 citation· ⚡1
It is found that GenAI has seen broad applications in other ways before, during, and after class, and its users speak fluently and readily to themselves, but still suffer from language bias, content accuracy, and learning bias.