Researchers who did not learn English as a first language face a documented, measurable burden in scholarly publishing that has nothing to do with the quality of their science. A 2023 survey of 908 environmental scientists across eight countries, published in PLOS Biology (Amano et al., “The manifold costs of being a non-native English speaker in science”), found papers by non-native English speakers were 2.5 times more likely to be rejected, and 12.5 times more likely to receive a request for revision, specifically because of the written English — and that early-career non-native speakers spend substantially more time preparing manuscripts and presentations in English than their native-speaking peers. AI proofreading and language-editing tools have become one real, low-cost way to narrow that gap. This guide covers what these tools can genuinely do for a non-native English researcher preparing a manuscript, where they fall short, and what journals now expect authors to disclose when AI has been used to polish a manuscript’s language.
The language burden, and why it matters for who gets published
The Amano et al. study is one of the more rigorously documented accounts of this burden, but it echoes what non-native English-speaking researchers have long reported anecdotally: manuscripts can be scientifically sound and still draw a rejection, or a heavier revision workload, because reviewers and editors read imprecise phrasing as a sign of imprecise thinking. The study also found that a substantial share of non-native English speakers avoid presenting at international conferences at all, citing lack of confidence in spoken English — a related but distinct cost from manuscript writing. None of this is a claim that non-native English speakers write worse science; it is a language-access problem layered on top of the scientific one, and it falls unevenly across the field by geography and institutional resourcing.
Professional human language-editing services (many journals and publishers, including Nature Portfolio, operate or recommend one) have long been the standard remedy, but they cost money and time that not every researcher, department, or country has. AI proofreading tools do not replace that option, but they lower the cost floor for a first pass considerably, which is the equity case for taking them seriously rather than dismissing them as a shortcut.
What AI proofreading and language-editing tools actually do
Two rough categories are worth distinguishing:
- General-purpose grammar and style checkers — tools like Grammarly or DeepL Write, built for broad writing use, not specifically academic prose. They catch subject-verb agreement, article usage (a persistently hard area for speakers of languages without articles, such as many East Asian and Slavic languages), preposition choice, and awkward phrasing.
- Academic-specific writing and language-editing tools — built specifically for manuscript text, trained or tuned on published scientific writing, and generally better at flagging register (is this too informal for a journal?), field-appropriate terminology, and structural phrasing common in IMRaD-style writing. CASRAI’s companion guide, AI Tools for Improving Academic Writing Style, covers this category and the specific tools in it (Paperpal, Jenni AI, and similar) along with the integrity line between polishing your own writing and letting a tool draft it — read that guide alongside this one for tool-level detail; this page focuses specifically on the non-native-English-speaker use case and the disclosure question.
For a non-native English researcher specifically, the practical value tends to concentrate in the mechanical layer: catching article and preposition errors, subject-verb agreement, verb tense consistency, and awkward literal translations of an idiom from the author’s first language. These are exactly the error categories the Amano et al. study and related language-access research identify as recurring friction points with reviewers, and they are also the category AI grammar tools are, at this point, genuinely reliable at catching.
Where these tools fall short
Three honest limitations are worth naming plainly, because overselling AI language tools to exactly the researchers who need the most help is its own kind of harm:
- Loss of voice and nuance. Aggressive rewriting can flatten a researcher’s own argumentative structure or hedge language into a generic, homogenized register, sometimes stripping out the precise epistemic qualifiers (e.g., “these results suggest” versus “these results demonstrate”) that carry real scientific meaning. A tool optimizing for “clean” prose has no way to know which qualifier the author intended.
- Over-correction and false “errors.” Field-specific terminology, non-standard-but-correct constructions common in a subdiscipline, and deliberately technical phrasing are frequently flagged as mistakes by general-purpose tools trained on broad, non-scientific text. Accepting every suggested change without judgment can introduce inaccuracies a subject-matter expert would not have made.
- They do not fix structural or argumentative problems. Sentence-level polish cannot substitute for a coherent argument, an appropriately scoped claim, or a manuscript structure that meets a journal’s expectations — see CASRAI’s guidance on copyediting for how this stage differs from substantive or developmental editing.
The practical implication: AI proofreading tools work best as a first pass that narrows the gap before a co-author, mentor, or professional editor reviews the manuscript — not as a final, unsupervised step before submission, particularly for a paper’s most consequential claims.
What journals require you to disclose about AI-assisted language editing
Editorial bodies moved quickly after 2023 to set boundaries on generative AI use in manuscript preparation, and language-polishing tools sit inside that broader policy landscape even though many publishers draw a distinction between basic language editing and substantive AI-generated content:
- The ICMJE Generative AI Policy, added to the ICMJE Recommendations in the May 2023 update (Section II.A.4), holds that AI tools cannot be listed as authors because authorship carries accountability a non-human tool cannot bear, and requires that AI use in writing be disclosed — a position followed or referenced by thousands of biomedical and health-science journals beyond ICMJE’s own member journals.
- The Nature Portfolio AI Policy requires authors to document generative AI use in the manuscript and is notably stricter than most competing publisher policies on AI-generated images specifically.
- The IEEE Generative AI Policy is not one single centrally branded document; consistent rules are disseminated through the IEEE Author Center and through parallel guidance from individual IEEE technical societies, so authors submitting to a specific IEEE venue should confirm the exact wording for that venue.
- Publishers including Elsevier and Springer Nature generally require a disclosure statement describing where and how generative AI was used, prohibit its use to generate or alter research data or images, and hold authors fully responsible for the accuracy of any AI-assisted content, regardless of disclosure.
The practical grey area for non-native English researchers specifically: many journal policies distinguish basic grammar/spell-checking (generally treated the same as using a word processor’s built-in spell-checker, not requiring disclosure) from AI tools that substantively rewrite, paraphrase, or restructure sentences (increasingly expected to be disclosed, often in the acknowledgments or methods, under language such as “the authors used [tool] to improve readability and language”). Because the exact line varies by venue and is genuinely still moving, the reliable approach is to check the specific journal’s author instructions or AI policy before submission rather than assume last year’s rule still applies, and to keep a record of which tool was used and for what, so a disclosure statement can be written accurately if required. For the broader legal and cross-publisher landscape beyond any one journal’s house policy, see CASRAI’s AI Disclosure Laws: The Legal Landscape vs. Publisher Policy.
Using AI proofreading tools responsibly: a practical approach
- Check the target journal’s policy before you submit, not after — policies differ by publisher and are revised frequently. If in doubt, disclose.
- Use AI tools for a first pass, not a final one. Have a co-author, mentor, or professional editor review the output, especially in sections carrying the paper’s central claims.
- Review every suggested change rather than accepting in bulk — especially around field-specific terminology and epistemic language (“suggests” vs. “demonstrates” vs. “confirms”).
- Keep a record of which tool you used and how, so an accurate disclosure statement is easy to write if the journal requires one.
- Don’t use a general AI writing assistant to generate new scientific content, data interpretation, or citations — every major publisher policy treats language polishing and content generation as different things, and only the former is routinely permitted without extensive disclosure.
Frequently asked questions
Do I have to disclose using Grammarly or a similar grammar checker?
Most current publisher policies treat basic grammar and spell-checking the same way they treat a word processor’s built-in spell-checker — not something that needs disclosure. The distinction most policies draw is between correcting existing text and having a tool substantively rewrite or generate new text; the latter is where disclosure expectations apply. Confirm against the specific journal’s current author instructions, since this is one of the faster-moving areas of editorial policy.
Can AI proofreading tools replace a professional language-editing service?
Not reliably for a manuscript’s most important sections. AI tools are strongest at mechanical, sentence-level correction; a professional editor or a fluent co-author is still better positioned to judge whether a passage reads as intended to a subject-matter reviewer, and to catch the over-correction and voice-loss issues described above.
Will using an AI proofreading tool get my paper flagged by an AI-content detector?
Light grammar correction is unlikely to trigger AI-generated-text detection, which is tuned to flag substantially AI-authored passages rather than AI-corrected ones, though detector reliability varies. See CASRAI’s guide to why AI-detection tools produce false positives for more on how these detectors work and their real error rates.
Are there tools built specifically for non-native English academic writers?
Several academic-specific writing tools market themselves specifically to non-native English-speaking researchers and are tuned on published scientific text rather than general prose, which tends to make them more accurate on academic register and terminology than general-purpose consumer tools. CASRAI’s AI Tools for Improving Academic Writing Style guide covers this tool category in more depth.
