Reducing AI Score for Journal Submission
A flagged manuscript can stall before a single reviewer sees it. What actually moves an AI score at the point of journal submission, and what to disclose to the editor no matter what the number says.
A manuscript can now stall before a single reviewer ever opens it. iThenticate, the publishing-grade version of Turnitin, has carried an AI writing indicator since April 2023, and it plugs directly into the manuscript trackers a journal already runs every submission through, including ScholarOne and Editorial Manager. An editorial assistant sees the percentage before an editor is assigned, let alone a reviewer. Learning how to reduce AI score for journal submission is less about beating one specific tool and more about understanding what that number actually reacts to, and what you owe the journal by way of disclosure regardless of what it says.
We're not trying to game a detector. Next are the handful of things that reliably move a score down without damaging the manuscript. And finally, there is one thing worth doing regardless of what your own report shows: tell the journal, in the wording it actually asks for, what you used and how.
Why Do Journals Screen for AI Writing Before Review?
Most journals that screen for AI writing also check for plagiarism at the same time because their pipeline touches all submissions. Since 2023, iThenticate has been offering its AI writing indicator as a paid add-on, built into the Similarity Report editors are already reading, designed to function within the document tracking tools journals use to run submissions through. The percentage is visible before anyone is assigned to read the argument itself.
That timing is what makes the number worth taking seriously before you submit, not after. A flag at the editorial-office stage does not get argued in front of a review panel, because there is no panel yet. For the mechanics behind why a score moves at all, TextPulse's guide to how AI detectors work covers the underlying measurement; what changes at the journal stage is timing and consequence, not the metric itself. A flag here produces a hold, a query email asking you to account for the score, or a quiet slide to the bottom of the queue, and the manuscript never reaches the point where the argument gets to speak for itself. The safest final layer before submission is also the oldest one: expert human editing, the single revision step no indicator can mistake for a machine.
How to Reduce AI Score for Journal Submission at the Manuscript Level
TextPulse's guide to using an AI humanizer for academic writing works through each section of a paper on its own terms, since an abstract, a methodology and a discussion trip a detector for different reasons. Read that first if the per-section mechanics are new to you, then come back here for what changes once every section is locked and you are looking at the whole manuscript, ready to go out the door.
A few things move a whole-manuscript score that section-level editing will not touch. A last-minute paragraph bolted onto an otherwise hand-revised paper, often a rushed introduction or a hurried conclusion, stands out precisely because it is smoother than everything around it. Repeating the same transition at the top of every section, opening five sections in a row with some version of "this section presents", flattens the document's rhythm even when every sentence inside is original. Calling the same variable three different names because sections were drafted weeks apart does something similar: it reads as sloppy to a human reader, and the resulting patchy rephrasing often reads as generated to a classifier.
A rushed introduction usually gives itself away in exactly this shape. "This paper examines the relationship between X and Y" opens a paragraph the same way dozens of other papers in the same issue might open theirs. "We expected X to predict Y, and in the second cohort it did not" is a rougher sentence than the first, and a markedly less predictable one, which is closer to what the indicator actually scores than quality is.
The practical fix is a single read-through of the finished manuscript, start to finish, after every section-level edit is done and before you touch anything again. Read for rhythm rather than content: does every paragraph run to roughly the same length, does every section open the same way, does a sentence you wrote in week one sound like it belongs next to one you wrote in week six. A burstiness checker applied to the whole file, not section by section, will catch the flattening that a per-section pass misses, because it measures variation across the document rather than within one paragraph.
If that read turns up a section that is uniformly flat rather than just one rough paragraph, an AI humanizer tool aimed at that section specifically is a faster fix than rewriting it from nothing by hand, provided you read the result afterward and correct anything it changed that you did not mean to change. It is a tool for finishing your own draft, not a substitute for having one.
Humanize your own paper
Transform your AI-assisted text and make it sound human, without touching important words or citations.
What Should You Disclose Regardless of Your AI Score?
Disclosure is a separate requirement from your score, and most publishers now attach it to every submission whether or not any tool was involved in drafting. Elsevier's own generative AI policy for journals, updated in June 2026, asks for a specific declaration section, titled "Declaration of generative AI and AI-assisted technologies in the manuscript preparation process", placed immediately before the reference list. It names the tool used, the purpose it served, and the extent of the author's oversight.
IEEE asks for something similar in a different place: the acknowledgments section, naming the AI system used and identifying which parts of the article it touched. Both policies draw the line in the same spot. Checking grammar, spelling and punctuation doesn't need a declaration, because both publishers treat that as editing. A pass that rewrites phrasing or sentence structure falls inside the declaration requirement, whatever your own AI score comes back as. The declaration is closer to a methods note than an admission: which tool, for what, and how much you relied on it.
Other journals request this information again in plain English in the cover letter: "Was any part of your paper created with a generative AI tool? If yes, which one?" Think of it as making one disclosure in two places that a journal will look at. Don't think of it as two disclosures that can conflict with each other. Treat the two as one disclosure written twice in the two places a journal is likely to look, rather than as separate obligations that might end up contradicting each other.
A Pre-Submission AI Score Checklist
The order matters nearly as much as the list itself. Working out of sequence usually means redoing an earlier step once a later one changes the manuscript again.
| Step | What it catches or covers |
|---|---|
| Finish every section-level edit first | Locks the prose so the remaining steps measure the real manuscript, not a moving target |
| Read the whole file straight through for rhythm | Uniform paragraph length and repeated section openings, the pattern a per-section edit misses |
| Run a burstiness check across the full document | Sentence-level variance a manual read can miss in a long file |
| Draft the generative AI declaration | Matches the journal's required wording and placement before you format the rest of the submission |
| Keep drafts and version history on hand | Evidence to answer an editor's query if the score gets flagged after submission |
What Happens If the Editorial Desk Flags Your Manuscript Anyway?
Answer the query with process, not an argument about the metric itself. A percentage is hard to rebut on its own terms, because no publisher has stated a threshold that reliably separates a careful human writer from a generated one. What you can offer instead is a version history, dated drafts, or notes showing the argument was yours before any tool touched a sentence.
It is reasonable to ask which tool produced the number and, where the journal will share it, to see the report itself. Vendors disagree with each other constantly, so a specific percentage from one classifier is an output of that classifier on that day, not a settled fact about your manuscript. Knowing which tool flagged you tells you whether the concern is about the whole paper or one section it weighted heavily.
What follows a submission is its own document, and there is a response to reviewers letter template for it. For the paper itself, humanizing a research paper end to end is covered separately.
Keep the response proportionate to what was actually asked. An editorial assistant flagging a score wants an account of your process, not a rebuttal of the tool's own methodology, and a defensive multi-page reply to a one-line query tends to read worse than a short, factual one. TextPulse reports an estimated Human Score computed from your own text, not a verdict from whichever tool the journal runs, and treating either number as a ruling rather than one input is a mistake in both directions.
Related research: the humanizers discussed above are compared head to head in A Controlled Comparison of AI Text Humanizers on Academic Writing, a TextPulse Research working paper with open data and code.
Frequently Asked Questions
The fastest way to reduce AI score for journal submission is a manuscript-level rhythm pass rather than more section-level rewriting: one read-through after every edit is finished, checked with a burstiness tool across the whole file, catches the uniform pacing that raises a score. Results and methodology stay exactly as written; only the surrounding prose changes.
Content planner and copywriter at TextPulse. Sara runs the blog day to day, from planning and drafting through to publishing. She writes the practical guides: clear explanations of academic writing problems, aimed at the person who actually has to hand something in.
