AI Content Detector

How This Detector Works (Plain Language)

This detector evaluates text using multiple stylometric indicators, which are measurable patterns in sentence structure, repetition, entropy, and phrasing behavior that often differ between human-only writing and AI-assisted writing. The output is a probability signal, not proof of authorship.

The same passage can produce different scores depending on topic, formatting, text length, editing depth, and whether multiple authors/styles were mixed together. Use this output as one review signal, not a final verdict.

What The Score Means

• 0-30 (Low AI-likelihood): pattern profile looks closer to common human variation.
• 31-70 (Mixed/Unclear): signal is inconclusive or contains blended characteristics.
• 71-100 (Higher AI-likelihood): stronger clustering of AI-like writing patterns.

Important: a high score can still be human-written (false positive), and a low score can still be AI-assisted (false negative).

Best Practices For Better Accuracy

• Use 200 to 500+ words where possible because short text is noisy.
• Test one author or style at a time instead of mixed sections.
• Remove long quotes, tables, and code blocks before analysis.
• If text was heavily rewritten, expect confidence to drop.

Limitations

• Performance varies by language, genre, and prompting style.
• Paraphrasing and rewriting can reduce detectable patterns.
• No detector should be the sole basis for academic, hiring, legal, or disciplinary decisions.

Responsible Use

Use this tool for quality review and transparency workflows, not for deception or unsupported accusations. High-stakes decisions should include human review and additional evidence.

FAQ

Why do short texts show lower confidence?

Short passages contain too little signal. Confidence increases when enough style data is present.

Can AI-written text look human?

Yes. Strong prompting, editing, and paraphrasing can reduce detectable AI pattern clustering.

Why does formatting change the score?

Formatting affects tokenization and structure signals, especially in short or mixed-content inputs.

What is the difference between score and confidence?

Score reflects AI-likelihood strength; confidence reflects signal clarity and stability for that sample.

Should I test multiple chunks and average?

Yes. Testing multiple coherent chunks gives a more stable view than relying on one sample.

Can I use this for policy enforcement alone?

No. Use this as one signal in a broader review process with human judgment.

Review Workflow for Teams and Publishers

If you run an editorial team, agency, classroom, or marketplace, do not use a single score as a pass or fail gate. A reliable process combines machine signals with manual checks and context around the author, purpose, and revision history.

Recommended 3-Step Process

1. Screen: run a first pass to identify low-confidence or high-risk passages for review.
2. Verify: compare flagged sections against writing samples and revision notes.
3. Decide: record final outcome with human reviewer notes and evidence.

What to Log for Better Consistency

• Document length, language, and domain (news, academic, marketing, technical).
• Model confidence band and indicator values at review time.
• Any edits made before or after detection.
• Final reviewer decision and reason.

Tracking these fields over time makes your policy more defensible and reduces inconsistent reviewer outcomes. It also helps identify where detector signals are strong versus where false positives are more likely.

Examples: How to Interpret Real Scenarios

Scenario A: Short product description (120 words)

A short commercial paragraph returns 78/100. Because the text is brief and repetitive, this should be treated as weak evidence. Expand to 300+ words from the same author before escalating.

Scenario B: Long research note (900 words)

The draft returns 62/100 with mixed indicators. In this case, the safer interpretation is "unclear signal". Request source notes, verify citations, and sample two additional chunks before making conclusions.

Scenario C: Edited AI draft

A heavily edited AI-assisted draft can score low if sentence variety and lexical behavior become less uniform. This is normal and is exactly why scores should not be used as proof.

Publishing and Academic Integrity Guidance

For publishers, educators, and compliance teams, the safest posture is transparent policy language. State clearly that this detector is used for triage and quality review, not as an automatic punishment system. Users should understand what data is evaluated, what limitations exist, and how they can appeal a decision.

• Publish a short policy explaining score ranges and human review requirements.
• Allow users to submit drafts, notes, or revision history when scores are disputed.
• Run periodic calibration checks on known human and known AI-assisted samples.
• Update policy language if false positive rates rise for your content category.

This approach protects trust, reduces inconsistent decisions, and keeps your workflow aligned with responsible AI use standards.