AI Detection and Grant Applications: Disclose and Defend Your Work

Funders increasingly ask grant applicants to declare AI assistance. The honest answer is usually simple: disclose editing help, keep the technical argument and data yours, and be able to show your working. What sinks proposals is not the disclosure itself but a mismatch between what you claim and what the text looks like.

What do funders actually ask about AI use?

Most funder AI policies are still catching up, but the questions cluster. They ask whether you used generative AI at all, which tools and for what tasks, whether any text was produced by a model rather than written by you, and how you verified accuracy. Some ask you to confirm that the intellectual contribution is yours. A few ban AI outright in the narrative and allow it only for language polishing.

Read the specific scheme’s guidance before you write a word of the declaration. If the funder has no policy, say so in your cover note and state your own practice. That is stronger than silence, because silence looks like concealment if a detector later flags a paragraph.

Disclosure is not a confession. It is a description of your method. “I used a language model to tighten sentence flow in sections 2 and 3; all technical claims, data and citations were written and verified by me” is a complete and defensible statement.

How do you separate AI-assisted editing from AI-generated content?

The distinction that matters is whether the model produced the idea or merely the phrasing.

• AI-assisted editing: you wrote the sentence, the model suggested a shorter version, you accepted or rejected it. The claim, the number, the citation and the judgement are yours.
• AI-generated content: the model produced the reasoning, the structure of the argument, or a claim you cannot trace to your own data. Even if you edit it later, the intellectual content originated elsewhere.

The first is normal professional practice, comparable to a colleague’s red pen. The second is the thing funders are actually worried about, and the thing that a detector flag will make look worse.

Before you run a detector on a 30-page proposal, know what you are looking at. DeAIze scores the whole document and highlights each sentence by AI probability, so you can see which lines carry the signal rather than guessing. The free tier gives you 200 words on signup, no credit card — enough to test a paragraph. See pricing if you need the full document.

Why does a detector flag a proposal you wrote yourself?

Because detection is a probability estimate, not a verdict. Detectors look at patterns: uniform sentence length, low burstiness, predictable word choices, hedged transitions. Technical writing is full of these anyway. A methods section written in the passive voice, with parallel sentence structures and standard terminology, will look statistically smooth even when a human wrote every word.

Our own measurement on 2026-09-13 is a useful reality check. We scored 23 hand-written DeAIze guides with our local detector at a 30% flag threshold: mean AI score 21%, median 22%, range 0-46%, with 5 of 23 flagged. On 12 unedited model outputs from fixed prompts, the mean was 33%, median 34%, range 4-46%. The distributions overlap heavily. A single flagged sentence tells you almost nothing on its own.

If a funder runs your proposal through a detector and sees a high score, the useful response is not to argue about the tool. It is to show your drafts, your notes and your data trail. That is what a panel can actually evaluate.

How do you revise a proposal so the reasoning reads as yours?

Go section by section and ask who made the decision.

1. Aims and objectives. These must be your words. If a model drafted them, rewrite from your own notes until each aim names a specific outcome and a specific method.
2. Background and significance. Keep the literature synthesis in your voice. Add the one or two papers only you would cite, and say why they matter to your argument.
3. Methods. This is where detectors flag most often and where you have the least room to change terminology. Do not rewrite protocol language to sound human; keep the standard terms and instead vary the surrounding prose. Our guide on editing a grant proposal without changing terms walks through that constraint.
4. Feasibility and risk. Add the concrete contingency you actually discussed with your team. Models produce generic risk statements; you have real ones.
5. Budget justification. Numbers and justifications should trace to your own calculations. Check every figure against your spreadsheet.

A useful test: read a paragraph aloud. If you cannot say where the claim came from or why you chose that method, it is not yet yours.

What evidence should you keep to defend authorship?

Build the file before anyone asks.

Evidence	What it shows	Keep it where
Version history with timestamps	The argument developed over time, not in one paste	Your institutional drive or repository
Notes, outlines, reading annotations	The reasoning originated with you	Reference manager or notebook
Data files and analysis scripts	The technical content is your work	Project folder, versioned
Supervisor or co-author correspondence	Others saw the draft evolve	Email or project chat
A short methods note on AI use	Exactly what the model did and did not do	Attached to the submission

This is the same evidence you would assemble for any authorship question. It is also the reason to disclose early rather than quietly. A dated trail is far more persuasive than a late explanation.

If you want to understand how the signals are computed before you interpret a score, how AI detection works explains the mechanics without overclaiming.

When is assistance too minor to mention?

This is a judgement call, and the safest rule is the funder’s, not yours. If the scheme says declare all generative AI use, declare all of it, including grammar checking. If it says declare substantive use, then a spelling and grammar pass is normally not substantive.

Three questions help:

• Did the tool change meaning, or only surface? Surface changes are minor.
• Could a reader identify which sentences it touched? If yes, and the funder asks, say so.
• Would you be comfortable explaining it to a panel? If not, either change the practice or disclose it.

The trap is not the small edit. It is the small edit you hid after being asked directly. Funders care more about candour than about whether you ran a grammar checker.

What to do if your proposal is questioned

Stay factual. Provide the version history and the methods note. Explain what the tool did, in plain terms, and point to the parts of the argument that are demonstrably yours. Do not attack the detector; acknowledge that scores are estimates and that the question is about authorship, not about the tool’s accuracy.

If you used a humanizer to reduce false positives on your own writing, say that too. DeAIze’s humanizer rewrites only flagged sentences and keeps a rewrite only when semantic similarity says the meaning survived, then re-scores and reports before and after. That is an editing record you can show, not something to hide. It does not guarantee any particular score on any third-party detector, and it is not a way to pass off someone else’s work.

The strongest position in a funding review is the same as in any other: your ideas, your data, your argument, and a clear account of the tools you used to express them.

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Measurement note: figures in this article come from our own detector run on 2026-09-13 — n = 23 hand-written guides versus n = 12 unedited model outputs, median AI score 22% and 34% respectively. Method: /methodology/.