If ChatGPT helped you outline, summarise prior work, or polish the language of a manuscript, the prose now reads like ChatGPT in places your reviewers will notice. TextSight runs a section-by-section scan against the same patterns Nature, Science, IEEE, ACS, Wiley, and Elsevier screeners look for, then helps you rewrite the flagged sentences in your own voice without touching citations, equations, or technical terms. Pre-submission sanity check and authentic-voice calibration, not a detector workaround.
A research paper is not one document. It is seven sections with seven different AI-tell profiles. Abstract and Discussion carry the highest risk because they are open prose. Methods carries the lowest because dense technical writing absorbs the template signal. The scan reflects that.
Paste the abstract on its own. It is the highest-risk single block in the manuscript because reviewers and screeners read it before anything else, and because ChatGPT abstracts follow a tight four-move template that classifiers weight heavily. Aim for an Authenticity Score above 75 on the abstract before you move on to the body.
Introduction, Literature Review, Methods, Results, Discussion, Conclusion. Paste each section separately rather than the full manuscript at once. The sentence-level highlights tell you which paragraphs are pulling the section score down. In a typical ChatGPT-assisted manuscript, two to four paragraphs across the whole paper carry most of the signal; the rest is fine.
Light for Methods and Results, where precision matters more than rhythm. Balanced for Introduction and Discussion, where a register check helps. Maximum is risky on academic prose because it can flatten formal voice; reserve it for isolated red sentences after a Balanced pass.
Paste the revised sections back in and verify the score lifted. Aim for above 70 across every section, above 80 on the abstract and discussion if you want margin. Then disclose your AI use in the methods or acknowledgments per your journal's policy. TextSight does not interact with any journal pipeline and we make no promise about specific screener outcomes; we report our own score honestly and let you decide whether the manuscript is ready.
The AI rewriter was calibrated against a corpus of ChatGPT-assisted manuscripts across STEM, life sciences, and social sciences. The pattern that shows up in an Abstract is not the pattern that shows up in a Discussion. Knowing the profile helps you spend rewriting time where it matters.
ChatGPT abstracts follow a four-move template: background, gap, method, contribution. Each move is one sentence of 22 to 28 words, transitions are explicit. The fix is to compress background and gap into one sentence and lead with the finding, not the field. This is the single highest-yield rewrite in the manuscript.
The opening sentence is the biggest tell. "This paper presents," "This study investigates," "This paper proposes" appear in about 70 percent of generated introductions. Replace with the concrete problem or a finding that surprised you. The literature-context paragraph and the gap paragraph often read as separate template moves; merging them helps.
The highest over-flag section because it is citation-heavy and chronological. AI-generated lit reviews summarise one paper per sentence in citation order. Real reviews group three or four studies together by claim. Re-group by argument, keep citation tokens exact, and the section score usually moves 30 to 50 points without losing scholarly density.
The cleanest section by default. Dense technical prose with equations, variable names, and assay codes absorbs the template signal. Run Light mode only. If a sentence flags, rewrite it by hand rather than auto-rewriting, because precision-critical spans must survive the edit unchanged.
Results paragraphs that walk through tables read template by design, and that is fine; reviewers expect it. The flag risk is in the transitional sentences between table walks. The AI rewriter focuses on those and leaves the table-walk language alone.
The section that needs the most register attention. ChatGPT's hedging vocabulary ("Interestingly," "Notably," "These findings suggest," "Our results indicate") clusters here. The fix is to vary openings, anchor each paragraph in a specific number or a specific comparison to prior work, and name the limitation you actually worried about rather than a checkbox one.
Short and easy to rewrite from scratch if the score sits below 70. Drop "In conclusion," state the one finding that matters most, name the specific next experiment. The synthesis closer ("collectively underscore," "pave the way for") is one of the loudest tells in the manuscript and the easiest to remove.
Between 2024 and 2025, every major publisher updated its author guidelines on generative AI. The policies converge on the same line: AI assistance for outlining, summarising prior work, and language polishing is allowed if disclosed; AI-generated substantive content is not. A pre-submission scan catches sentences that cross that line before a reviewer does.
Disclosure required in methods or acknowledgments. LLMs may not be listed as authors. Internal classifier screening before peer review is documented at Nature and operates at several of the others without specific disclosure. A flag triggers an editor query about your AI use and can delay the review timeline by weeks.
AI-use statement required on every submission, naming the model and the sections it touched. ACM extends the policy to revisions, conference papers, and workshop submissions. IEEE flagged roughly 4 percent of its 2024 submissions for AI-content review based on internal screening, per its own published numbers.
Policies tightened in early 2025. ACS prohibits AI use for "creating or altering scientific content" and screens with both internal and third-party tools. Elsevier, Wiley, and Springer require disclosure across their journal portfolios. PLoS journals require a specific statement about whether AI tools contributed to text, images, or analysis.
The AI scan covers one risk; similarity screening covers a different one. Most journals run iThenticate or a Crossref Similarity Check report on submissions, which compares your manuscript against published literature and detects plagiarism or self-plagiarism. Pre-flighting both before submission is the sober move; the two reports rarely overlap and together they cover most of what desk review actually checks.
For academic prose the mode choice matters more than for any other content type. Maximum can flatten the formal voice journal reviewers expect, so the default we suggest is conservative and section-specific. Different sections want different modes within the same manuscript.
Light makes mild edits and preserves academic register, citation context, equations, variable names, and technical terminology. Score gains per pass are smaller, but the output still reads like a manuscript you would send to a journal. This is the starting mode for Methods and Results, and a safe choice on Introduction and the body of the Abstract.
Balanced runs moderate rewrites and shifts vocabulary and rhythm more aggressively than Light without flattening voice. It is the right choice for the Discussion section, the gap paragraph of the Introduction, and the closing-implication sentences of the Abstract. The places where ChatGPT's hedging register is loudest are exactly the places where Balanced helps most.
Maximum runs the most aggressive rewrite. The caveat is real on research-paper prose: aggressive rewrites can flatten the formal voice journal reviewers expect, replacing your distinctive phrasing with generic conversational patterns that read flat for an academic audience. Use Maximum on isolated red sentences after a Balanced pass has already done the work, not on a whole section.
The recommended sequence for a full manuscript: Light on Methods and Results, Balanced on Introduction and Discussion, Light first then targeted Balanced on the Abstract. Conclusion is short enough that rewriting from scratch is often faster than running any mode.
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Research papers are the use case where the line between legitimate AI-assisted writing and academic dishonesty matters most, because the reputational and disciplinary stakes are highest. We want to be explicit about which side of that line we are on.
Manuscripts you authored, where ChatGPT was used as an outline assistant, a literature summariser, or a language polisher inside your journal's disclosure policy. The research is yours, the analysis is yours, the argument is yours. The AI rewriter helps you catch sentences where the assistant register leaked into the prose so the submitted manuscript reads in your voice rather than the ChatGPT voice. This is closer to a careful proofread than to anything else.
We make no promise that TextSight will get any specific manuscript past Nature's classifier, IEEE's screener, or any other journal pipeline. We report our own score honestly and explain what it means. If a section is mostly ChatGPT and only lightly edited by you, our scan will tell you that and the AI rewriter will not magically fix it; it cannot put authentic analysis that was not there. The score and the highlights are diagnostic, not laundering.
Even after authenticity, if you used ChatGPT for outlining, lit-review summarising, or language polishing, disclose it in the methods or acknowledgments as your target journal's policy requires. Detection of undisclosed use is a far bigger problem than disclosed-and-cleaned-up use. The AI rewriter is not a substitute for the disclosure statement; it is the polish step you run before the disclosure statement.
Generating substantive research content with ChatGPT, attaching your name, and submitting to a journal. That breaches the policies of every major publisher regardless of which AI rewriter you run the output through. We will not pretend otherwise. If that is the situation you are in, we would rather you used the detector to understand which paragraphs read AI and then rewrote them with the analysis you actually performed.
If you are advising on whether TextSight is appropriate for your group, the framing is: same scope as a grammar checker or a journal language-editing service. Legitimate as a self-check on disclosed-use language polish, not legitimate as a way to disguise generated substantive content. The detector itself is also available for lab-wide use at the Business rate.
The detector workflow tuned for thesis chapters, journal submissions, and grant prose.
Open the detector →The shorter-form coursework version of this workflow, calibrated for student essays.
Open essays page →The flagship AI rewriter page covering all source content. Three modes, closed-loop calibration.
Open AI rewriter →How the score is computed and what threshold to aim for before journal submission.
Read the guide →Free to try, no card. Section-by-section workflow, three modes, citations and equations preserved, ESL-aware calibration.