Jul 2026· Arab World English Journal· Vol 17, pp. 278-287· 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
This paper examines how the syntactic complexity of academic writing is affected mainly by the source of authorship (AI-generated or human) or by the genre of disciplinary writing (linguistic or literary). The primary purpose is to test the syntactic-complexity differences between these variables and to establish the degree of influence of genre conventions on structural variation. The importance of the research is that it adds to the existing discussions about AI as a phenomenon in academic writing and, specifically, whether AI-generated texts are capable of syntactically reproducing the specific norms of a specific discipline of writing. To this end, the comparative corpus-based design was used. The sample consisted of 20 introduction sections: equal numbers of linguistic and literary texts and equal numbers of human-authored AI-generated texts. The Second Language Syntactic Complexity Analyzer (L2SCA) extracts fourteen syntactic complexity measures, which include length of production unit, subordination, coordination and phrasal sophistication. The results indicate that the complexity of syntax is genre-based and not source-based. Although no differences were found to be constant in both AI-generated and human academic introductions in linguistic data, there were much higher levels of subordination in literary texts of human origin. In general, the discipline genre had a more significant effect on syntax variation than the authorship source. The paper suggests the implementation of genre-sensitive models to assess AI-written academic texts and recommends additional studies that would use a bigger sample and discourse analysis.
Asia A. Alheety, Meethaq Khamees khalaf, Hussam J. 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
Artificial Intelligence (AI) has emerged as an increasingly influential tool in second language acquisition (SLA), offering new opportunities for personalized language instruction, immediate feedback, and more interactive learning experiences. Although it has been widely adopted, limited studies have focused on examining whether AI-generated language input aligns with established theories of SLA, particularly Krashen’s Input Hypothesis. The mixed-methods study investigates the extent to which AI-powered learning tools support the learners’ language development by providing comprehensible input (i+1) as proposed in Krashen’s input hypothesis. Quantitative data were collected from 40 first-year students of the Department of English at Shaheed Benazir Bhutto University, Shaheed Benazir Abad (SBBU SBA) using 13-item Likert-scale questionnaire, while qualitative data were collected from 38 screenshots of learner–AI interactions analyzed using content analysis. The quantitative results indicated that the learners perceived AI tools as effective in providing clear language input, immediate feedback, vocabulary support, and increased motivation for language learning. The qualitative results revealed that 60% of AI-generated responses aligned with Krashen’s i+1 principle, 25% were below the learners’ current proficiency level, while 15% exceeded the learners’ current level of proficiency. The findings suggest 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). The study contributes to the growing body of literature on SLA by linking established theories with AI-powered language-learning tools. It provides practical implications for educators, curriculum designers, and AI developers seeking to design more adaptive and pedagogically sound language-learning systems.
Nooe Fatima Lashari, Guo Fengmeng, Ayaz Ahmed Maganhar· International "Journal of Ac...· 0 citations
This scoping review primarily aims to synthesize empirical research on artificial intelligence in English language teaching and learning published from 2015 to 2025. The main question investigates how the integration, applications, and pedagogical roles of AI have evolved over the past decade. 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. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidance, 129 empirical studies were identified and analyzed using descriptive mapping and thematic analysis. The main findings indicate three overlapping evolutionary phases: an early system construction phase focused on tutoring, a mobile-and-voice phase emphasizing speech practice, and a generative phase dominated by large language models. The evidence base remains heavily concentrated in higher education, where AI frequently acts as a tutor, practice partner, or co-writer. For further use, this study recommends adopting teacher-mediated task designs, shifting assessments to focus on the learning process, and prioritizing longitudinal research in primary and under-resourced educational settings.
Ese Emmanuel Uwosomah· Arab World English Journal· 1 citation· ⚡1
As generative large language models like ChatGPT are developing, their applications have become increasingly widespread and there are new technical directions for traditional English oral language teaching. However, studies have focused on writing and grammar and their systematic review of oral English teaching is still lacking. This article discusses generative large language (LLMs) models in English oral linguistic teaching, with a particular emphasis on their usage in different teaching situations and their impact on learning results. The article also compares development directions and differences between research in countries and outside the world. 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. Based on this, the paper suggests future development directions from the perspective of technological optimization and teaching integration in order to provide a systematic guideline for empowering English oral Language teaching with artificial intelligence.