Check for AI Writing: How to Spot AI-Generated Text and Verify Originality

If you need to check for AI writing, you are usually trying to answer one question: was this text likely generated or heavily assisted by an AI tool, and is it safe to publish or submit?

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Quick answer / TL;DR: Treat AI detection as a risk check, not a verdict. Combine text clues, context clues, and a detector score if the stakes are high.

A dependable workflow to check for AI writing

1) Start with the goal and the risk

Low risk: you just want a quick gut-check for a blog comment or caption.
Medium risk: you are reviewing a guest post, client draft, or ad copy that must sound human.
High risk: academic integrity, legal/compliance writing, or anything where a false accusation would seriously harm someone.

2) Use three signals (triangulation)

Text signal: does the writing stay vague where a human would normally be specific (names, dates, constraints, real examples, trade-offs)?
Truth signal: do the claims hold up when you spot-check sources, data, and quotes?
Process signal: can the author show outlines, drafts, tracked changes, or notes that explain how the piece evolved?

If you decide to use a detector, give it enough text to work with. Some university guidance notes detectors are less accurate on short passages and points to vendor documentation suggesting around 400+ words for more stable results. Limits can change—check the platform help center for the latest.

Working with tight formats? Use counters first: Meta title length, Meta description length, Word count, and Character count basics.

Decision table: pick the right level of checking

AI Writing Check Table

What you are checking	Best signal mix	What to collect	What you can reasonably conclude
Short snippet (headline, caption, meta description)	Manual clues + context	Author history, brief, revisions	Detectors are shaky on short text; focus on consistency and plausibility
Long-form article or essay	Manual clues + truth checks + detector score	400+ words of the main body, plus a few random paragraphs	You can estimate likelihood and decide whether deeper review is needed
Academic submission (high stakes)	Process evidence + oral follow-up + cautious detector use	Draft history, outlines, notes, and a short viva-style Q&A	You can assess authorship patterns without relying on one score
Publishing/agency review (many drafts)	Batch scanning + plagiarism checks + human sampling	Automated reports plus spot-checks on sections that look generic	You can triage at scale and focus human time where risk is highest

Bottom line: a clean workflow beats vibe-based accusations. Next, you will learn a step-by-step method you can use even without any detector.

Run a quick originality scan

Use a pre-publish check to spot likely AI-assisted passages and potential plagiarism before you hit publish.

Scan my draft

Step-by-step: how to check for AI writing (without any detector)

Underline specifics: highlight every concrete detail (names, dates, locations, numbers, constraints). AI-assisted drafts often look polished but stay strangely non-committal.
Stress-test the logic: ask what would change the conclusion, what trade-offs were considered, and what evidence supports each claim. Weak reasoning often shows up as confident-but-generic statements.
Verify the hard claims: pick 3–5 factual statements and validate them against primary sources. AI-written text can sound fluent while making subtle errors or inventing references.
Check for repetition in disguise: look for the same idea restated in different words, especially in list-heavy sections and templated conclusions.
Look for human fingerprints: a real author usually has idiosyncrasies (unusual examples, sharp opinions, or a distinct voice). AI outputs often converge toward neutral tone and safe phrasing.
Ask for process evidence: request an outline, notes, drafts, or tracked changes. For high-stakes cases, a short oral follow-up about their choices (why this structure, why these sources) can be more reliable than any score.
Compare with known writing: if you have past work from the same person, compare sentence length, punctuation habits, and how they handle uncertainty.
Document your decision: write down which signals you saw and what you did to verify them. This reduces the chance of a snap judgement.

How to use an AI detector responsibly

Detectors typically output a probability-style score. Use that score to decide where to look closer, not to declare authorship. A cautious approach:

Run checks on multiple chunks (intro, middle, conclusion) instead of one paste.
Prefer longer samples over short snippets.
Be extra careful with non-native English writing and highly formal writing, where false positives are more likely.
Treat mixed results as a signal to review, not a contradiction to resolve by re-scanning until you get the answer you want.

Mistakes to avoid when checking for AI writing

Assuming style tells are proof: tidy grammar, smooth transitions, or certain punctuation can appear in human writing too.
Ignoring context: if you do not know the brief, the author, or the editing process, the text alone rarely settles the question.
Over-trusting percentages: a single number can hide uncertainty, language effects, and model drift.
Skipping plagiarism checks: AI-assisted drafts can still include copied phrases or overly close paraphrases, even when the draft looks original at a glance.
Turning detection into punishment: for education and workplaces, clearer policies and better assessment design often work better than catching people out after the fact.

A practical way to verify originality at scale

If you publish, teach, or run a content team, the real problem is volume: you need a repeatable way to triage drafts quickly and focus human review where it matters. One option is Originality.ai, which combines AI detection with plagiarism checking for a pre-publish trust check.

Scan AI-assisted drafts: flag sections that look likely AI-generated so an editor can review tone, specificity, and claims.
Reduce duplicate-content risk: run plagiarism checks to catch accidental overlap before it becomes a publishing problem.
Team and bulk-friendly: useful when you review many submissions or manage multiple writers.

If you want a simple next step after doing the manual checks above, you can scan your draft for AI signals and plagiarism before you publish. As of October 2025, Originality.ai help docs describe credit-based scanning where a credit covers roughly 50–100 words depending on settings.

Review guest posts at scale

Audit submissions

FAQ

Can you really tell if something was written by AI?

Not with certainty from text alone. You can make a better judgement by combining content clues, fact-checking, and evidence of the writing process.

Are AI writing detectors accurate?

They can be useful as screening tools, but they produce both false positives and false negatives. Accuracy also varies by text length, topic, language, and how the text was edited.

Why do detectors disagree with each other?

They rely on different models, training data, and thresholds. Even the same detector may change behavior over time as models and methods update.

How much text should I paste into a detector?

More is usually better. Practical guidance often suggests using a few hundred words or more and scanning multiple chunks instead of only the intro.

Can AI-assisted text pass as human?

Sometimes. Light edits, added specifics, and mixed authorship can reduce a detector signal. That is why process evidence and spot-checking claims matter.

What should I do if my human writing is flagged?

Do not panic. Save drafts, outlines, and revision history, and be ready to explain your research and decisions. A single detector result is not definitive proof.

Conclusion: a safe next step

To check for AI writing reliably, use a repeatable workflow: start with your risk level, triangulate text clues + truth checks + process evidence, then use a detector score only as a final screening signal.

When in doubt, assume the score could be wrong and prioritize transparency, documentation, and clear editorial or classroom expectations.

If you are publishing or reviewing many drafts, a tool like Originality.ai can help you triage at scale with AI detection plus plagiarism checks, while keeping humans in the loop for final judgement.