ChatGPT as a Peripheral Language Editor: A New Path for Non-Native English Researchers

For millions of scientists around the world, the hardest part of publishing research is not the experiment or the analysis, but the language. English dominates global scientific publishing, and researchers who grew up speaking other languages routinely see their manuscripts criticized for grammar, vocabulary, and style rather than for the quality of the science itself. A new study published in Discover Education argues that generative artificial intelligence, specifically ChatGPT, could become a powerful equalizer, provided the human author remains firmly in charge.

The research, led by Md. Saiful Alam of the World University of Bangladesh together with colleagues from institutions in Bangladesh and Malaysia, frames the relationship between non-native English-speaking researchers and ChatGPT as an epistemic collaboration with a strict division of labor. The authors describe ChatGPT’s role as peripheral and non-authorial: the tool edits language, corrects grammar and punctuation, and suggests rewording, but it never generates the science or claims credit for the work. The human researcher, meanwhile, retains what the team calls optimal authorial anthropocentricity, meaning full ownership of the ideas, the content, and the final text.

The stakes are high. Non-native English speakers make up roughly 95 percent of the global population, and previous studies cited in the paper show they face delays, rejections, and significant financial costs when publishing in English-only journals. Journals often recommend paid language editing by native English speakers, a service that can burden researchers in the global South who frequently publish on their own budgets without institutional funding. The lead author recounts one such recommendation from a journal, which told him his article needed proofreading by a native English speaker to correct grammatical and syntactical issues reducing fluency.

To explore whether ChatGPT could offer an alternative, the team conducted a qualitative study grounded in a constructivist paradigm, treating the authors’ own lived experiences as data. They composed short research paper excerpts containing deliberate language issues, ran them through QuillBot’s free grammar checker to identify errors, and then asked the free version of ChatGPT 3.5 to rewrite the texts in correct English. The conversations were captured as images using Snip & Sketch and analyzed with adapted techniques from digital data mining and educational data mining, including text categorization, segmentation, and topic mapping.

The results were striking in their specificity. In one demonstration, a 31-word paragraph written by a non-native researcher contained five errors flagged by the grammar checker, involving word choice, prepositions, and punctuation. When the same text was given to ChatGPT with a simple request to rewrite it in correct English, the tool corrected every flagged error without adding or subtracting any content. Crucially, the authors note, ChatGPT did not create a new text; it revised the existing one, which means composition credit, and therefore authorship, remains with the human writer.

The study also documents where the tool falls short, and these limitations define the researcher’s responsibilities. ChatGPT does not reliably edit in-text citations to match APA conventions, sometimes expands abbreviations incorrectly, and can apply unrequested styling such as double quotation marks around well-known terms, which conflicts with APA norms. In one example, the tool misinterpreted the abbreviation M-SGTs as Multilingual Student-Generated Teachers when the authors had intended Multi-Subject Generalist Teachers. Because the model cannot see the entire paper or grasp its contextual nuances, the researchers argue, a careful human review of every AI-edited passage is essential before anything is incorporated into a manuscript.

Beyond mechanical fixes, the findings suggest ChatGPT can help researchers internalize the conventions of scientific writing itself. By comparing their original drafts with the edited versions, authors can notice patterns, such as the correct punctuation after the word including, and gradually master the tone, voice, and discursive style expected by high-profile journals. The team describes this as noticing-based grammar learning, a process that may reduce reliance on the tool over time and strengthen the researcher’s independent command of academic English.

The practical outcomes reported are notable. According to the paper, fifteen non-native English-speaking researchers applied the proposed workflow and subsequently published fifteen papers in English-only journals indexed in Scopus and Web of Science, without receiving editorial requests about language quality or being compelled to purchase native-speaker editing services. The authors report that the tool’s performance in correcting language errors reached a 99 percent accuracy rate in their demonstrations, and that the approach aligns with publisher policies, including statements from major platforms that generative AI is not an author and should only be used to improve language and readability with caution.

The team is careful to position the framework within ethical boundaries. Because ChatGPT’s participation is limited to language editing, the collaboration falls within what most scholars and journals consider ethical AI use, though the authors recommend disclosure that the tool provided digital labor for language purposes. The three principles underpinning their model are the pragmatization of the language problem, the deployment of ChatGPT’s digital instrumentality, and the retention of the researcher’s optimal authorial anthropocentrism, ensuring that creation, not mere generation, remains the defining feature of scientific knowledge production.

The authors acknowledge the study’s limitations: it rests on a small sample of personal, first-hand experiences and simulated conversation data rather than large-scale empirical evidence. They call for longitudinal, positivist studies to establish generalizability. Still, the paper offers a provocative vision for the future of scientific publishing, one in which a free AI tool serves as a peripheral editing partner, researchers from the global South gain equitable access to English-language journals, and the chronic linguistic gatekeeping of academic publishing begins to lose its grip.

Subject of Research: Using ChatGPT as a peripheral language-editing tool to support non-native English-speaking researchers' scientific writing

Article Title: Framing an epistemic collaboration between chat GPT’s peripheral instrumentality and the author’s optimal anthropocentrism for enhancing non-native-English-speaking researchers’ scientific writing

Article References: Alam, M. S., Asmawi, A., Haque, M. H., Fatema, S., Ullah, M. M., Patwary, M. N., Fatema, T., & Akhter, H. (2026). Framing an epistemic collaboration between chat GPT’s peripheral instrumentality and the author’s optimal anthropocentrism for enhancing non-native-English-speaking researchers’ scientific writing. Discover Education, 5(1), Article 1148. https://doi.org/10.1007/s44217-026-02263-6

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02263-6

Keywords: ChatGPT, generative AI, scientific writing, non-native English speakers, academic publishing, language editing, authorship, linguistic justice, Discover Education, epistemic collaboration, global South, research ethics