AI Writing Detector
Check grant and research proposal text for AI-like writing signals. Private, browser-only, and designed for revision rather than accusation.
Add text to analyze
Paste any passage or upload a document. The score updates automatically, and everything runs locally in your browser.
Uploads support selectable-text PDFs, DOCX, TXT, Markdown, and HTML. Files are parsed locally before the detector runs.
Paste text to begin
Revision directions
- Paste at least 80 words before treating the score as meaningful.
- Use full paragraphs from the same section. Short abstracts, bullet lists, and captions are easy to misread.
AI writing detector guide for researchers
Last updated: .
What is an AI writing detector?
An AI writing detector estimates whether a passage contains patterns often associated with machine-generated text. The Proposia detector is intentionally framed as an AI-like writing signal checker: it reports risk bands, confidence, chunk evidence, and revision directions instead of claiming to prove who wrote the text.
That distinction matters for research proposals. Grant writing is already formal, polished, and structured, so a simplistic detector can confuse careful academic style with generated prose. Use this tool to find paragraphs that sound generic, over-smoothed, repetitive, or low in project-specific evidence before a reviewer sees them.
How this browser-only AI detector works
The current Proposia tool uses a fast local analysis path: sentence variation, lexical variety, repeated phrasing, generic academic phrases, transition density, punctuation range, and concrete-detail signals such as numbers, methods, acronyms, and citation-like evidence. The file extractor supports pasted text plus TXT, PDF, DOCX, Markdown, and HTML uploads. Everything runs in the browser. Your pasted text or uploaded document is not sent to Proposia, OpenAI, or any third-party model.
The design follows the practical direction of recent detector research: combine multiple weak signals, abstain on short passages, and show local evidence rather than a single unsupported verdict. Research systems such as GLTR popularized token-level evidence, while methods such as DetectGPT, Fast-DetectGPT, and Binoculars show why probability-based detection is powerful but often too heavy for a simple browser-first user experience.
Why AI detector scores should not be used as proof
AI-generated text detection remains probabilistic. OpenAI retired its own classifier after reporting a low rate of accuracy, and independent research has shown that detectors can fail under paraphrasing, domain shift, short text, and multilingual writing. A widely cited study also found that GPT detectors can unfairly flag non-native English writers at higher rates.
For that reason, this tool avoids a binary human-versus-AI label. The safest workflow is to treat the score like a smoke alarm for revision: inspect the highlighted chunks, add concrete details, verify citations, disclose AI assistance when rules require it, and preserve a transparent writing process.
How to reduce AI-like signals in a grant proposal
The best way to lower an AI detector score is not to make prose worse. It is to make the proposal more specific, more verifiable, and more clearly tied to your project. Replace general claims with named methods, pilot data, sample sizes, study sites, work packages, milestones, funder criteria, and explicit risks. Reviewers reward the same qualities that make detector scores more trustworthy: specificity, evidence, and accountable reasoning.
- Add concrete evidence: preliminary data, instruments, cohorts, numbers, citations, or named protocols.
- Replace generic phrases such as "significant impact" with the specific outcome, population, endpoint, or policy lever.
- Vary sentence rhythm naturally by mixing decisions, caveats, methods, and reviewer-facing rationale.
- Remove repeated paragraph templates. Each aim, work package, or deliverable should earn its own structure.
- Check citations and factual claims manually. AI detector scores do not validate truth.
Where Proposia fits in the writing workflow
Proposia helps researchers turn ideas, calls, and constraints into funder-aware proposal drafts. This detector is a companion material: use it after drafting to find places where the prose sounds too generic for peer review. Then revise toward specificity: why this team, why this method, why this funder, why now.
For a complete workflow, pair this AI writing checker with the Text-to-Space Estimator, Citation Converter, and Risk Assessment Matrix. When you need a full proposal draft rather than a detector signal, start from the Proposia proposal generator.
Frequently asked questions
Can this AI detector identify ChatGPT with certainty?
No. It can flag stylistic patterns associated with AI-like prose, but it cannot prove whether ChatGPT, Claude, Gemini, or any other system wrote a passage. Edited AI text and polished human text can overlap.
Why does the detector ask for at least 80 words?
Very short passages do not provide enough sentence variation, vocabulary variety, repetition, or specificity evidence. A title, caption, abstract fragment, or bullet list can easily produce a misleading result.
Can I upload PDF or DOCX files?
Yes. This browser-only version supports pasted text plus TXT, PDF, DOCX, Markdown, and HTML uploads. PDF extraction works best on selectable-text PDFs such as exported proposals, papers, and reports. Scanned PDFs may contain little or no extractable text unless OCR is added in a later workflow. DOCX uploads use raw-text extraction, not HTML conversion.
References and further reading
- OpenAI, New AI classifier for indicating AI-written text.
- Gehrmann et al., GLTR: Statistical Detection and Visualization of Generated Text.
- Mitchell et al., DetectGPT: Zero-Shot Machine-Generated Text Detection.
- Sadasivan et al., Can AI-Generated Text be Reliably Detected?.
- Liang et al., GPT detectors are biased against non-native English writers.