This guide explains how Scribbr detects AI writing, what its result can and cannot tell you, and how to write naturally in your own voice. A detector result is a statistical estimate, not proof of who wrote a passage, and no rewrite can guarantee any particular Scribbr outcome.
Your result will appear here.
For students, researchers, multilingual writers, and editors reviewing a Scribbr AI detector result while protecting meaning, attribution, and compliance with assignment or publication rules. Check model-estimated signals with our AI Detector and refine wording with our Paraphrasing Tool.
Scribbr is an academic writing service whose free AI detector is aimed at students checking essays and theses before submission. Like other AI-text detectors, Scribbr compares patterns in your writing — word predictability, sentence rhythm and repeated phrasing — with patterns from its training examples, then reports a likelihood. That result is a statistical estimate, not proof of authorship. Check Scribbr's own documentation for current details: https://www.scribbr.com/ai-detector/
AI detectors broadly use statistical classification to assess whether text resembles material associated with AI generation. That is different from observing who wrote it. Human writing can receive an AI-associated classification, and generated writing can escape detection. Do not interpret a displayed percentage as a verified percentage of words written by AI unless the current interface explicitly defines it that way—and even then, it remains an estimate. Read Scribbr’s current explanation beside the result, including any language, length, or input restrictions. Features and presentation may change; this guide does not assume a proprietary detection mechanism. An AI-writing assessment also answers a different question from a plagiarism or source-overlap check. Neither establishes whether a quotation is accurate or an argument is supported. Investigate those matters separately.
Before using Humanizely, save your original draft and list facts that must survive unchanged: citations, quotations, dates, quantities, units, qualifications, and findings. Submit a passage with a specific editing objective, such as clarifying who performed an experiment or replacing an unsupported generalization. Humanizely’s free allowance is 500 words per request; keep the surrounding context available when working in chunks. Review a Standard-mode revision as a proposal, not an approved replacement. Compare it sentence by sentence with your draft and underlying sources. Reject invented examples, stronger causal language, altered statistics, or newly implied achievements. Preserve technical terminology when a synonym changes the meaning. Humanizely supports 15 languages, but that does not establish Scribbr’s support for every language. Check Scribbr’s current requirements for the text you intend to evaluate.
Validation should start with the assignment and evidence, not another detector run. Does the revision answer the prompt? Can each factual claim be traced to a source or your own records? Do citations still support the sentences they accompany? Read the passage aloud to catch awkward transitions, but do not add errors or random personal anecdotes to influence a classifier. Keep dated drafts, notes, source annotations, and version history. If AI assistance is permitted, record what tool you used and what it changed, then follow the required disclosure format. If a Scribbr result triggers a concern, ask for human review and provide relevant process evidence. If assistance is prohibited, do not use Humanizely to conceal it; revise independently or ask the instructor what support is allowed.
Write from your own notes and outline, in your own voice. Vary sentence length the way you would when speaking, cut filler transitions, and add specific details you can verify. Keep drafts and sources so you can explain your process. Follow your school's or employer's AI policy, and review any tool's output before you use it.
Original fictional educational examples—not customer results
Before
It is important to note that peer feedback may improve revision quality in some classroom settings (Morgan, 2022).
After
Peer feedback may improve revision quality in some classroom settings (Morgan, 2022).
Removing the empty opening makes the claim direct without strengthening it. Morgan is a fictional citation for this educational sample; in actual work, check the source and retain both the qualification and reference.
Before
The experiment was conducted using trays measuring 30 × 20 × 5 cm, and observations were recorded daily from 3 to 17 April 2024.
After
The experiment used trays measuring 30 × 20 × 5 cm. Observations were recorded daily from 3 to 17 April 2024.
Splitting the sentence separates equipment from observation procedures. All dimensions, dates, and frequency remain intact. Check the laboratory log before publication; do not invent the observer, sample size, or measurement protocol.
Before
During my internship, I made a significant contribution by creating a spreadsheet that reduced weekly reconciliation errors from 12 to 7.
After
During my internship, I created a spreadsheet that reduced weekly reconciliation errors from 12 to 7.
The revision removes self-praise while preserving the stated achievement and figures. Verify the numbers and causal attribution against records. For admissions use, follow the institution’s authorship rules; retain only experiences that are genuinely yours.
Save the exact text submitted to Scribbr alongside the result so later revisions do not obscure what was assessed.
Edit for a named problem—unclear reference, repetition, or unsupported inference—not for an imagined detector preference.
Leave direct quotations unchanged and verify quotation marks, page references, and attribution after rewriting.
Avoid testing isolated fragments when they remove context; follow Scribbr’s current input guidance.
Do not upload confidential research, student records, or employer material without permission and a review of relevant privacy terms.
No. Humanizely cannot guarantee detector outcomes. Assess a revision by its accuracy, clarity, and compliance, not a promised classification.
No. A statistical classification is not proof of authorship or misconduct. A fair review should consider drafts, evidence, applicable policies, and the writer’s explanation.
Repeated rewriting can distort claims and erase your voice. Stop when the text meets your editorial criteria; an unresolved flag calls for review, not endless substitutions.
Only within the institution’s rules. Your experiences and substantive writing must be original. Disclose assistance when required, and never use rewriting to hide prohibited AI use.
Check factual meaning, citations, numbers, units, dates, and the strength of conclusions. Reject any sentence you cannot explain or support yourself.
Consult Scribbr’s current detector page and help documentation, plus your institution’s policy. Product guidance explains the tool; institutional rules determine acceptable use in your submission.
