If ChatGPT helped you outline a need statement, polish a project description, or clean a budget narrative, NIH, NSF, NIH-NIAID, DOE, Wellcome Trust, MacArthur, Ford, Open Philanthropy, and the Gates Foundation now require human-authorship attestation or AI disclosure on the cover form. TextSight runs a section-by-section scan calibrated for the six reviewer-facing patterns that flag grant narrative, recommends a target score per section type, and surfaces the exact sentences a programme officer or reviewer panel will react to. Pre-submission sanity check before IRB, grant office, and funder portal. Not an attestation workaround.
Between 2024 and 2025, every major federal agency and most large private foundations updated their submission policy on generative AI. The policies converge on one line: AI assistance for outlining, literature synthesis, and language polishing is allowed when disclosed; AI-generated substantive content is not, and an AI-flagged proposal can be rejected outright. The score is the pre-submission check that keeps an honest draft on the right side of that line.
Human-authorship attestation required on submitted narratives as of 2025. NIH-NIAID added explicit research-integrity language flagging AI-generated content. Specific aims and approach sections attract the closest reviewer review because templated approach paragraphs dilute the experimental hypothesis. Programme officers do not confirm automated detection on every R01, but reviewer panels increasingly note AI-shaped prose in their critiques, and several review sections are briefed on common AI tells before the meeting.
Wellcome Trust requires AI disclosure on the cover form for major research programmes. MacArthur asks applicants to attest to authorship on the application portal. Ford added AI disclosure to its grant submission system during 2025. All three reviewer panels include AI-shaped prose in their evaluation notes regardless of whether the formal policy text mentions it. The defensible posture is to pre-scan, rewrite reds, then disclose honestly on the cover form.
Open Philanthropy and the Gates Foundation added AI disclosure questions to their application portals during 2024 and 2025. Community foundations and smaller family foundations are following at varying speeds. Treat every funder as if AI disclosure matters and let the scan history be the evidence trail that supports an honest disclosure or a no-AI claim.
A flag is rarely fatal on its own, but reviewers who suspect AI use score the application more harshly on innovation, significance, and investigator capability. An integrity query from a programme officer can delay a review cycle by weeks, and a confirmed undisclosed-use finding ends the application and damages the relationship with the agency. For a six-figure ask, the pre-submission scan is the cheapest insurance you can buy.
The score is not a generic AI probability. It is a calibrated reading of the six patterns reviewer panels actually flag on a scroll-through, weighted by section type. A clean composite score means a draft that survives the patterns reviewers learn to spot within the first page.
Outputs feeding into outcomes feeding into impact is templated by definition, and ChatGPT writes it with identical clause structure across three paragraphs. The risk is not the framework, which most funders ask for; it is the identical sentence rhythm across each link in the chain. The scan flags repeated clause structure and recommends varying construction with concrete actor language.
Three or four consecutive sentences each opening with a statistic and a clause about underserved populations read as generated paragraphs rather than authored narrative. Reviewers spot the pattern on a scroll-through. The scan highlights the stack and recommends compressing to one statistic per paragraph, anchoring the rest in a specific community, partner organisation, or evaluation finding from your prior work.
"Comprehensive, evidence-based, community-driven." "Innovative, scalable, sustainable." "Rigorous, equitable, replicable." Tripled adjectives are an AI tic reviewers spot on a scroll-through. Each adjective does no work on the page. The scan surfaces every triplet and recommends replacement with the concrete mechanism: who benefits, on what timeline, against which baseline.
"Systemic," "transformative," "scalable," "synergistic." ChatGPT defaults to these words whenever it needs to flag an initiative as serious. Reviewers parse them as evidence the team has not figured out the mechanism. The scan flags the cluster and recommends naming the handoff: what data does Year 1 produce that Year 2 needs, on what timeline, with which partner.
Most grant narratives ship from three to seven contributors: lead PI, co-PIs, programme staff, evaluator, finance for budget narrative, and sometimes a contracted writer. Each contributor enters AI assistance at a different point and the combined draft reads uneven, which itself reads as AI. The sentence map surfaces the paragraphs where one author leaned on ChatGPT harder than the rest.
Stock language about governance, oversight, and organisational capacity recycled from board materials, then polished by ChatGPT, reads as boilerplate to programme officers who read fifty applications a season. The fix is to replace governance abstractions with one specific decision your board made about this work, and one specific way your evaluator will report against it.
A grant narrative is not one document. It is seven sections with seven different AI-tell profiles. Need Statement and Project Description carry the highest rhetorical risk and need the highest scores. Methods and Logic Model absorb the template signal because of procedural and framework density and can sit lower without raising concern. The scan recommends a target per section.
The highest-yield single scan in the proposal. ChatGPT need statements stack three or four statistics with underserved-population clauses; reviewer panels spot the pattern within the first paragraph. A score above 75 means the section reads in your organisation's voice rather than as generated paragraphs. Below 75, compress to one statistic per paragraph and anchor the rest in your organisation's lived work with the population.
The narrative spine of the proposal. ChatGPT's tripled adjectives and theory-of-change abstractions cluster here because the section invites broad claims about impact. A score above 75 means the section names concrete mechanisms paragraph by paragraph rather than gesturing at impact. Below 75, the sentence map shows exactly which paragraphs need rewriting.
The cleanest section by default. Dense procedural prose with assay codes, equipment models, statistical tests, and partner-protocol numbers absorbs the template signal. A score in the 55 to 70 band is normal and acceptable. The sentence map is the signal, not the headline score; rewrite any individual red sentence rather than chasing a higher composite.
Templated by definition because outputs-outcomes-impact is the framework most funders ask for. The risk is identical clause structure across three paragraphs, which reads as generated even when each link in the chain is real. Aim 60 to 75 with the sentence map clean; vary construction across the chain and embed concrete actor language.
Evaluators are usually less practised at sentence-level voice than the lead PI, and the section carries domain vocabulary that overlaps with templated AI phrasing: formative, summative, mixed-methods, developmental. Aim above 70 on the narrative paragraphs; the instrument descriptions can sit lower because they are procedural by design.
Notoriously stiff by default, and ChatGPT cleans it into something even stiffer. Aim above 70 with dollar figures, percent effort, calendar-month allocations, personnel names, and IRB protocol numbers all preserved on the sentence map. Replace stock connective phrasing with specifics: instead of "personnel costs include the PI and a graduate student," write "0.6 calendar-month PI effort plus one 12-month graduate student stipend at the institutional rate."
The section PIs hate writing and therefore the section most heavily AI-edited in a final draft. Stock language about diversified funding, long-term partnerships, and institutional commitment recycles word for word across ChatGPT drafts. Aim above 75 and add one specific sentence about what happens to the resource once funding ends and which partner takes on which line item.
Most grant packages clear three internal checks before they reach the funder: the writer's own draft pass, the IRB or development office compliance review, and the institutional grant office sign-off. The TextSight pre-submission scan fits before the first internal review, so the version your IRB and grant office read is already AI-clean and you are not iterating on attestation-sensitive language under deadline pressure.
Write in your usual editor: Word, Docs, or the funder portal. Using ChatGPT for an outline pass, a literature synthesis, or to break writer's block on a difficult section is the realistic 2026 default and inside the disclosure regime most funders allow. Compose the prose itself in your own voice or the organisation's voice from your notes, prior funded proposals, and conversations with programme staff.
Open app.textsight.ai, paste each narrative section, and scan. Free tier handles 1,500 words. Pro handles 10,000 characters per scan, which fits a typical narrative subsection like a need statement or a logic-model paragraph. For a 12-page R01 Research Strategy, split by section: specific aims, significance, innovation, approach, broader impacts. The scan returns in well under a minute with an Authenticity Score, a sentence-by-sentence colour map, and a target-score recommendation per section type.
Red sentences are the priority. A section scoring 60 with three red sentences is far easier to fix than a section scoring 75 with twelve yellow ones. The colour map tells you exactly where to spend revision time. Replace generic claims with concrete ones from your organisation's work: a citation, a prior pilot result, a named collaborator, a specific population, a number.
Send the consolidated package to your IRB, development director, or institutional grant office for compliance review. Pro history keeps every section scan for 90 days. PDF export gives you a contemporaneous record useful if a programme officer asks downstream how the narrative was produced. A typical narrative subsection round-trips in about fifteen minutes; a full 12-page narrative in about ninety. The submission portal then sees a draft that has already passed three layers of internal review.
Grants are rarely funded by a single reader. NIH study sections, NSF panels, Wellcome interview boards, MacArthur juries, and most foundation programme committees use review panels: three to seven reviewers per application, scoring independently, then comparing in a panel meeting. The applicant never sees the discussion, only the final aggregate score and the written summary.
If one reviewer raises an AI-tone concern, a second often confirms it, and the application drops in the queue before the science or programme design is debated. The first reviewer needs to flag it only once for the bias to set in. Competing proposals that read cleanly do not carry that handicap. A pre-submission scan that catches the patterns before submission removes the first reviewer's flag entirely.
Many funders triage in early rounds, where applications in the bottom half are not discussed at all. An AI-flavoured draft that would have scored well on substance can be triaged out before reaching the substantive discussion, simply because the prose lowered the initial reviewer scores. The fix is upstream, in the pre-submission scan, not at the panel meeting where the applicant has no voice.
Several programme officers run informal AI screening on a sample of submissions before assigning them to study sections or panels. A flagged narrative triggers an integrity query that delays the application by a review cycle and surfaces the issue with the agency in writing. A scan-cleaned draft never sees that query.
Many funders now ask about AI use somewhere in the submission. Honest disclosure of limited AI use, outlining, lit-review summarising, light editing, reads far better than caught heavy use. Disclosure is also easier when the prose itself reads in your voice. Reviewers are lenient about disclosed light use and harsh about prose that reads AI-generated whether or not it is disclosed.
All section calibrations available on every paid plan. Pro at $19.99 a month standard, $14.99 a month on yearly billing, fits solo grant writers and consultants pushing 20 to 50 applications a year. Business at $39.99 a month, or $29.99 a month on yearly, fits nonprofit development offices and research administration. Full details on the pricing page.
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The post-scan rewrite workflow: three modes, section-by-section recommendations, numbers and named partners preserved.
Open the AI rewriter →The detector workflow tuned for NIH, NSF, Wellcome, Ford, and MacArthur narratives.
Open the detector →Sister page for thesis, dissertation, and journal submission scoring with IMRaD calibration.
Open papers page →How the 0-100 score is computed and what threshold to aim for before funder submission.
Read the guide →Free to try, no card. Section-by-section workflow, target scores per section type, IRB-and-grant-office pre-flight ready.