The results revealed relevant differences between the analyzed papers: texts produced in 2023 showed greater stylistic variation, the presence of authorial markers, and irregularities typical of human writing, whereas texts from 2025 presented a higher concentration of indicators associated with linguistic standardization, structural uniformity, and a reduction of individual markers of authorship.
The growing presence of artificial intelligence (AI) in higher education has changed the way students approach academic writing. While AI-powered tools offer practical support in generating ideas, organizing texts, and refining language, their increasing use has also raised concerns about the originality and authenticity of students’ written work. This study aims to examine how AI influences students’ authentic academic writing skills and to identify the patterns of dependence that emerge during the writing process. A convergent mixed-methods design was employed by integrating quantitative data from questionnaires completed by 71 third-semester English education students in Makassar, Indonesia, with qualitative evidence drawn from fifteen empirical and conceptual studies published between 2024 and 2025. Descriptive statistics were used to analyze the survey data, whereas thematic synthesis was applied to the literature findings. The results indicate that students rely on AI to varying degrees across different stages of academic writing. Five interrelated forms of dependence were identified, namely dependence on idea generation, language and text organization, revision and editing, cognitive and metacognitive processes, and writing autonomy. However, the findings suggest that AI is not inherently responsible for weakening students’ writing abilities. Instead, the erosion of authentic writing tends to occur when technological assistance replaces the reflective, critical, and self-regulatory processes that are central to academic writing. These findings underscore the importance of developing educational practices that encourage students to use AI responsibly while maintaining intellectual ownership and academic integrity.
Muhammad Yahrif, Suharti Sirajuddin, Muhamad Khaedar· Al Qodiri : Jurnal Pendidika...· 0 citations
The rapid spread of large language models (LLMs) has significantly transformed academic writing practices and actualized discussions about authorship, language quality, and academic integrity. At the same time, diachronic changes in academic discourse during the active implementation of generative artificial intelligence remain insufficiently studied. The study combines a systematic literature review with a corpus diachronic analysis of authentic academic annotations, allowing us to trace long-term trends in the development of academic discourse. The aim of the work is to identify linguistic changes in academic writing during 2015–2025 and determine the role of human editing in quality assurance of AI-assisted scientific texts. The research material was a corpus of 870 English-language annotations of scientific articles indexed in the Scopus database in the field of arts and humanities. Quantitative linguistic analysis was carried out using Python tools. Indicators of lexical density, lexical diversity, syntactic complexity, average sentence length and frequency of cohesive markers were analyzed. A statistically significant increase in lexical density and frequency of cohesive markers has been revealed, indicating an increase in information compression and explicit discursive organization of texts. Indicators of traditional lexical diversity and syntactic complexity remained relatively stable. The observed trends are consistent with characteristics described in AI-assisted writing studies; however, the study design does not allow them to be directly related to the use of large language models. Human editing remains a key factor in ensuring factual accuracy, discursive coherence, lexical enrichment, and academic integrity. The results can be used to develop practices for the responsible use of generative AI in academic communication.
T. Nedashkivska, I. Varvaruk, M. Podoliak et al.· Journal of Intelligent Decis...· 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
The article examines the phenomenon of the development of academic writing among students of schools. The authors studied the essential characteristics of academic writing. Academic writing is defined as the creation of written texts in academic discourse, the organization and expression of the original knowledge obtained according to the research criteria of a certain scientific field, in relation to the specifics of the subject of cognitive activity. The formats and requirements for the specifics of writing an academic text are highlighted, such as: building on the basis of a model with a clear structural subdivision of structural and semantic blocks, lexico-grammatical fragments, stylistic features, logical alignment of arguments of one’s own opinion and explanation of evidence. The technical requirements for a correct and standardized academic text based on short paragraphs are defined. Features of keyword design, prohibition of personal pronouns, and more. The authors analyzed the difficulties of using academic writing at the present stage due to the local and non-systemic nature of its development in Kazakhstan. The school should lay the foundations of literacy, including reading (the ability to find, understand and critically evaluate information), writing (the ability to produce and organize their own thoughts), mathematical skills (the ability to think logically and operate with signs), but in fact it is not able to provide students with even a basic set of academic writing skills. The authors analyzed the causes of academic illiteracy (memorization and memorization, lack of interdisciplinary connections and non-linguistic from the point of view of literacy of presentation in oral or written form, the nature of knowledge assessment in most subjects, the profile of training).
M. A. Zhanzakova, G. Zhylkybay, A. Syzdykbayeva· BULLETIN of the L N Gumilyov...· 0 citations
The findings revealed that in evaluations conducted without a rubric, teachers were limited in their ability to distinguish individual differences and demonstrated low scoring consistency, while in evaluations conducted using a rubric, scoring consistency increased in both groups, although, as in the first evaluation, artificial intelligence tools demonstrated a higher level of consistency.
Burak Asma· Language Teaching Research· 0 citations
This thesis investigates the vocabulary and incidental vocabulary learning opportunities in authentic academic texts for postgraduate students of Artificial Intelligence. Book chapters and journal articles from five courses for taught masters of Artificial Intelligence at Victoria University of Wellington were collected to compile the corpus of Artificial Intelligence reading texts (CAIRT).
The thesis consists of three studies. The first study investigates the vocabulary profile of texts in CAIRT, assessing how much vocabulary is needed to reach 95% and 98% coverage using Nation’s (2012) 25 BNC/COCA word lists with five supplementary lists. The findings show that 4,000 and 6,000 word families plus supplementary lists are needed to reach 95% and 98% coverage, respectively. However, lexical demands vary across courses and text types, indicating that the vocabulary load cannot be generalised even within a single academic discipline. Additionally, the first 3,000 word families account for the largest proportion of the corpus, while mid-frequency word families and supplementary-list words make comparable contributions, with low-frequency word families accounting for the smallest proportion.
The second study explores the repetition and distribution of word families from each category (high-, mid-, low-frequency, and supplementary lists). Although high-frequency word families are most likely to recur, the majority of word families occur only a small number of times. While many word families are shared across courses and trimesters, a substantial proportion are restricted to individual sub-corpora, indicating uneven opportunities for incidental vocabulary learning across the texts and courses.
The third study investigates which words are elaborated within texts that may facilitate vocabulary learning and reading comprehension. Using Hyland’s (2005) taxonomy of code glosses, 57 single words and 188 multiword units (MWUs) are identified as elaborated within two courses. The elaborated single words include high-, mid-, and low-frequency words, abbreviations, proper nouns, transparent compounds, and other words outside the BNC COCA 25,000 word families. The corpus frequency rate of these elaborated words varies, and their distribution is inconsistent across texts. Many of these elaborated words occur in only one text, and a small number of them recur across multiple texts. Five single words and seven of the MWUs are elaborated multiple times, and most of the elaboration occurs within a single text.
Overall, the findings demonstrate that the lexical demands of authentic postgraduate AI readings are more nuanced than estimates of vocabulary coverage alone imply and cannot be generalised even within a single discipline. Although authentic disciplinary texts repeatedly recycle a relatively small core vocabulary, they provide uneven opportunities for incidental vocabulary learning through repetition and lexical elaboration. Compared with the adapted reading materials and authentic texts used in experimental studies of incidental vocabulary learning through reading, authentic AI readings provide these lexical conditions less consistently, as their primary purpose is to communicate disciplinary knowledge rather than to facilitate vocabulary learning. These findings contribute to a more contextualised understanding of incidental vocabulary learning in authentic disciplinary reading and have implications for vocabulary profiling, course sequencing, and pedagogical support for postgraduate AI students.