Paste a chapter, a section, or a full manuscript, see an Authenticity Score on a 0 to 100 scale, and read which specific sentences carry the AI signal. Calibrated section-by-section so Methods, which is uniform on purpose, is not penalised the way generic detectors penalise it, and Discussion, which is where genuine ChatGPT use shows up, is weighted where it matters. The recommended pre-journal-submission pre-flight: scan, revise the flagged paragraphs in your own voice, re-scan, then run iThenticate and Crossref Similarity Check before you submit. ESL-aware. .edu Pro is $13.99.
A research paper is not one document. It is seven sections with seven different AI-tell profiles. A whole-paper score averages those baselines and tells you almost nothing about where the work is. Section-by-section is the workflow that actually moves a manuscript from flagged to defensible.
Paste the abstract first. 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 (background, gap, method, contribution) that classifiers weight heavily. Aim for above 75 on the abstract before you move on to the body. A 250-word abstract can be 90 percent honest writing yet still flag because of one polished closing sentence; the scan finds those.
Introduction, Literature Review, Methods, Results, Discussion, Conclusion. Paste each section separately rather than the whole paper 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 AI signal, and the rest is fine. Targeting those paragraphs is the difference between an honest revision and a structural rewrite.
Open your manuscript alongside the highlights and rewrite the red sentences before reaching for any rewrite tool. Read each aloud. Replace one abstract claim per paragraph with a specific number, a named instrument, or a concrete observation from your own data. Vary sentence length so two adjacent sentences are not both in the 18 to 24 word range that classifiers weight. For a stubborn sentence inside a precision-critical span (a procedural clause, a definition), run the AI rewriter in Light mode on just that sentence rather than the whole paragraph.
Paste the revised sections back in and verify each section score lifted: above 75 on Introduction and Discussion, above 70 on Results, above 65 on Methods (Methods runs lower by design). Then run iThenticate or your institutional Crossref Similarity Check report as a separate pre-flight before you submit. The two checks cover different risks and rarely overlap; together they cover most of what desk review actually screens for. TextSight does not interact with either pipeline and we report our own score honestly so you can decide whether the manuscript is ready.
The scorer 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 per section helps you spend revision time where it actually moves the score.
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 because the abstract sets the reviewer's reading frame.
The opening sentence is the biggest tell. Sweeping openers ("In the rapidly evolving landscape of," "This paper presents," "This study investigates") appear in about 70 percent of generated introductions. Replace with a concrete finding that surprised you, a counter-intuitive observation, or a specific data point from your own results. Scan the first 200 words as a separate test and treat anything under 70 as a rewrite candidate.
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 in honest writing, the riskiest if AI-generated. Dense technical prose with equations, variable names, and assay codes absorbs the template signal, so a Methods score around 65 is normal. The classifier knows it is reading a Methods section if the structural markers are present and adjusts thresholds accordingly. A Methods score around 85 with Discussion at 45 is the worry pattern; that combination is what real undisclosed AI use looks like.
Statistical reporting language is templated for the same reason Methods is, and reviewers expect it. The flag risk is in the transitional sentences between table walks. The scorer focuses on those and leaves the table-walk language alone. Citation density and reference-heavy passages are stripped during scoring and re-inserted, so a passage with 40 citations in 600 words is scored on the underlying prose, not the citation noise.
The section that needs the most attention. Synthesising results, comparing to prior work, hedging limitations, and projecting future directions are all tasks where a tired author asks ChatGPT to write the Discussion. The output reads fluent and confident but lacks specifics. If your Discussion contains "underscoring the multifaceted nature of," "navigating the complexities of," or "this study contributes to a growing body of literature," scrub those first. Aim for above 80 here if you want margin.
"In conclusion, this study has demonstrated" is the single most common AI tell in academic prose. Short enough to rewrite from scratch if the score sits below 70. Drop the synthesis closer ("collectively underscore," "pave the way for"), state the one finding that matters most, and name the specific next experiment instead.
A number on its own does not tell you whether to submit. These five bands describe what the classifier is seeing per section, what publisher screeners tend to do with the same manuscript, and what the right next move is at each band.
Reads strongly human across all sections. Journal AI screeners are unlikely to flag the manuscript at the submission gate. Reviewers will read the prose without their AI-suspicion antennae triggered. Submit with the disclosure statement your target journal requires if you used AI assistance at any drafting stage.
Acceptable for conference papers and most journals. For Nature, Science, Cell, JAMA, Lancet, NEJM, and the field-leading venue in your discipline, push Introduction and Discussion to 80 or above with one more editing pass. Methods sections scoring in this range are normal and need no intervention.
Acceptable for Methods and Results given their structural uniformity. Not acceptable for Introduction, Discussion, or Abstract; those need to come up. Treat the discursive sections as the priority. A paper with Methods at 60 and Discussion at 78 is healthy; the reverse is the warning sign.
High probability that a journal AI screener flags the paper at the submission gate and returns it with a request to clarify AI assistance. If you used AI, disclose explicitly. Either way the discursive sections need rewriting before resubmission. Methods or Results scoring this low is unusual and suggests procedural language was AI-generated, not just prose.
Almost certainly raw or lightly-edited ChatGPT across the discursive sections. Any reasonable screener catches this. Submitting at this score risks desk-reject and, in graduate-thesis contexts, a referral to academic integrity. The fix is a substantive rewrite of Introduction and Discussion, not a quick edit. Use the sentence-level highlights to find every triggered paragraph.
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.
Policies evolve quickly. Always check the journal's instructions for authors as published at the time of your submission.
Most international researchers writing in English face structurally higher detector risk because non-native academic register overlaps with the patterns detectors learn from AI output. The scorer on this page is calibrated for that, not against it.
In our internal evals against a sample of human-written ESL research-paper sections, the average competitor detector returned a false-positive rate around 18 to 22 percent. The TextSight detector returns roughly 11 to 13 percent on the same sample. That is around 40 percent fewer false positives, not zero, and we report this honestly because the gap matters for international PhD students, postdocs, and researchers whose first language is not English. The score you see is the same score paid users see.
The same four steps, with the emphasis on Step 3 (revising the flagged paragraphs) rather than Step 4 (re-scanning). Read each flagged sentence aloud; ESL researchers gain more from this exercise than native writers because it surfaces sentences where formal academic register collided with non-native phrasing in a way that reads AI to the classifier. When you reach a rewrite pass, the tool defaults to Light mode and adjusts vocabulary away from idiomatic native-speaker phrasing so your second-language voice stays intact rather than getting flattened toward a register you do not use.
We do not try to make ESL manuscripts sound like native-speaker manuscripts. That would erase the writer. The goal is the same authentic-voice goal as for any researcher: catch sentences where assistant register leaked in, revise them in your own voice (including your second-language voice), and submit a manuscript that reads like you wrote it. The score is a pre-submission check, not a fluency exam.
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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 this scorer sits 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 scorer catches 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, Elsevier's screener, iThenticate, 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, the scan will tell you that and no AI rewriter pass will magically fix it; it cannot put authentic analysis that was not there. The score and the highlights are diagnostic.
Even after a clean scan, 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 scorer is not a substitute for the disclosure statement; it is the polish step you run before the disclosure statement.
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 scorer and AI rewriter are available for lab-wide use at the Business rate, with 5 seats and a 90-day audit trail.
The AI rewriter companion to this scorer: three modes calibrated for academic prose, citations preserved.
Open the AI rewriter →The detector workflow tuned for thesis chapters, journal submissions, and grant prose.
Open the detector →The sibling scorer for undergrad coursework: five-paragraph, argumentative, comparison, persuasive, narrative formats.
Open essays scorer →How the 0 to 100 score is computed and what threshold to aim for before a journal submission.
Read the guide →Free to try, no card. Section-by-section calibration, sentence-level evidence, ESL-aware, citations preserved. .edu Pro at $13.99.