Are we reading the researchers or the artificial intelligence? Transformations in academic writing after the expansion of generative AI

Linguistic Features of AI-Generated Academic Texts and the Role of Human Editing

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.

From "No Cap" To Empirical Truth: A Qualitative Inquiry into AI-Driven Register Translation as an Academic Writing Scaffold for Generation Z LG120 Undergraduates

With the rapid surge in the use of this new technology called generative artificial intelligence (AI), university students are also undergoing a transformation in their academic writing. The transition between informal social media and online communication and academic discourse may pose linguistic challenges for LG120 Generation Z undergraduate students whose daily communication is dominated by social media and digital culture. While AI-supported writing has already garnered increased academic interest, the prevailing focus in the current literature has been on ethical issues, academic integrity, and learning outcomes. The use of AI to aid transitions between various language registers is not as well-documented. This concept paper suggests a qualitative study of the academic writing scaffold of LG120 Generation Z undergraduate students in the scope of AI-based register translation. Within the scope of this inquiry, register translation is conceptualized as the strategic application of generative AI to transform colloquial digital vernacular into institutional academic prose, ensuring the preservation of the authorial intent. Furthermore, AI-based academic writing scaffolding encompasses the linguistic and cognitive assistance provided by these technologies, enabling undergraduates to navigate complex disciplinary codes through lexical refinement, syntactic restructuring, and the adaptation of formal stylistic conventions. The study adopts a sociocultural perspective on learning and frames the use of generative AI as a mediating tool, which can help facilitate students' ability to adapt colloquial language into more academic forms. The purpose of the proposed study is to examine how undergraduates are using AI for this purpose, the meaning(s) they are giving to these uses, and whether and how they think the reformulations are helping them to grow as academic writers. Using purposive sampling, approximately 12–15 LG120 undergraduate students who regularly use generative AI for academic writing will be recruited. Data will be collected through semi-structured interviews and analyzed using Braun and Clarke's (2006) thematic analysis to explore students' experiences and perceptions of AI-mediated register translation. The study aims to inform the ongoing debates around the pedagogical implications of the use of generative AI tools for academic literacy, conceptualized as students' ability to communicate effectively using appropriate academic language conventions, disciplinary discourse, and formal writing practices in learning in higher education, and to extend the knowledge about the mediating role of new technologies in the development of academic literacy. In this study, AI is viewed as an equitable learning scaffold that supports students in maintaining their authorial voice and intended meaning while adapting their writing to institutional academic conventions that maintain students' voice and intent while making institutional academic codes accessible.

The Flaw of Perfection: How University Professors Identify the Inauthentic Voice of AI

Generative AI in University EFL Writing Instruction: A Systematic Literature Review

Generative artificial intelligence (GenAI), particularly large language model-based tools such as ChatGPT, has rapidly entered university English as a Foreign Language (EFL) writing instruction. These tools can support brainstorming, outlining, drafting, corrective feedback, revision, and academic language refinement. Yet their use also raises concerns about over-reliance, authorship, academic integrity, assessment validity, and the changing role of teachers in writing pedagogy. This systematic literature review synthesizes recent evidence on GenAI in university EFL writing instruction using the PRISMA 2020 framework. Searches were designed for Scopus, Web of Science Core Collection, ERIC, and Education Source/EBSCOhost, covering publications from 1 November 2022 to 22 June 2026. After duplicate removal, title/abstract screening, full-text eligibility assessment, and quality appraisal, 120 studies were included in the qualitative synthesis. Narrative thematic synthesis identified six recurring themes: GenAI as a writing-process scaffold, GenAI-generated feedback, revision uptake and learner engagement, teacher-AI feedback alignment, academic integrity and authorship, and methodological limitations in the existing evidence base. The review concludes that GenAI is most educationally defensible when integrated as a guided formative-feedback resource rather than as a substitute writer or replacement for teacher expertise. Practical implications are offered for assignment design, AI-use disclosure, feedback literacy, prompt literacy, and process-based assessment.

Generative AI and lexical change in digital discourse: a systematic review of empirical linguistic research (2023–2026)

Generative artificial intelligence (AI) has rapidly emerged as a transformative force in digital communication, reshaping contemporary language use and accelerating lexical change across online environments. A growing number of empirical studies have examined various aspects of generative AI, including AI-assisted writing, digital discourse, language use, and computational linguistics, thereby, limited research has systematically synthesized how generative AI influences lexical change in digital discourse from a linguistic perspective. The present study aims to systematically review empirical linguistic research published between January 2023 and May 2026 to examine the influence of generative AI on lexical change, identify recurring patterns of lexical innovation, and synthesize the existing body of evidence. Guided by the PRISMA 2020 framework, the review analyzes 59 empirical studies collected from peer-reviewed journals indexed in the Web of Science Core Collection (WoSCC) using descriptive analysis and thematic synthesis. The findings reveal that generative AI accelerates lexical innovation through the emergence of AI-related terminology, promotes the diffusion of newly coined lexical items across digital platforms, and contributes to semantic expansion and diverse word-formation processes. Additionally, the review identifies recurring linguistic patterns that demonstrate the evolving relationship between AI technologies and contemporary digital communication. Future research should expand empirical investigations to multilingual digital environments and longitudinal contexts to develop a more comprehensive understanding of AI-driven lexical change across diverse linguistic communities.

An Analysis of Students’ Writing Ability in AI-Assisted Writing

The integration of Artificial Intelligence (AI) tools in writing instruction has garnered widespread attention; however, empirical evidence on the actual quality of student writing produced with AI assistance remains limited. This study aims to examine students' writing ability in AI-assisted writing products. This research employed a qualitative case study design. Data were collected through document analysis of narrative texts from five Grade XII students at SMAN 12 Sinjai, Indonesia. The analysis was conducted using Brown’s (2018) five-component framework, assessing content, organization, grammar, mechanics, and style. The findings revealed that students’ writing ability in AI-assisted contexts remains predominantly low. Only two students demonstrated Moderate ability, while the rest showed Poor ability across multiple components. Specific deficiencies included extreme repetition in content, lack of logical flow in organization, pervasive past tense errors in grammar, missing punctuation in mechanics, and severely limited vocabulary in style. The study concludes that a fundamental disconnect exists between AI use and writing quality, as students lack the foundational knowledge necessary to utilize AI tools effectively. These findings suggest that AI integration alone does not guarantee improved writing outcomes without corresponding foundational skill development.