Free tool

Check text for AI generation with sentence-level highlights

Paste an essay, article draft, email, or code comment and get an explainable verdict: highlighted sentences, the primary linguistic indicators that drove the result, and copyable summaries for reporting or review.

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Access

Free in-browser

Run one-off checks without signing up

Explainability

Sentence-level highlights

See exactly which lines influenced the verdict

Workflow

Copyable verdicts & CSV export

Quickly move results into review queues

Explainable signals, not just a score

How the detector works

The detector analyzes linguistic and structural patterns across the input and returns a human-readable verdict. Instead of a single opaque score, results show which sentences were most indicative of AI generation and list the primary indicators (e.g., repeated phrasing, unnatural transitions, citation anomalies). Use the highlights to guide human review and remediation.

Sentence highlights: up to five sentences flagged with inline explanations
Indicator list: linguistic and structural reasons behind the verdict
Plain-text verdict: human / AI / ambiguous plus a short reviewer note

Tailored prompts for reviewers

Prompt templates for common workflows

Use the detector with purpose-built prompt templates for different content types. Copy these templates directly when preparing text for review or automating CSV uploads.

Student essay check

For instructors validating submissions

Prompt template: "Analyze the following student essay for likelihood of AI generation. Highlight up to five sentences that most indicate synthetic authorship and explain three linguistic or structural indicators supporting the assessment. Output a short summary for the instructor and a plain-text verdict label."

SEO article audit

For content teams and editors

Prompt template: "Review this article draft and flag paragraphs that show patterns typical of AI-written SEO content (repetitive phrasing, generic transitions, lack of original examples). Provide a remediation checklist to make the piece more original and publisher-ready."

Email authenticity scan

For recruiters and compliance reviewers

Prompt template: "Assess whether this email appears AI-composed. Identify unnatural formality, improbable context references, or generic salutations, and recommend two verification steps for the recipient."

Code comment review

For engineering teams auditing commit messages and comments

Prompt template: "Check the following code comments and commit messages for AI-generated patterns (overly generic descriptions, perfect grammar, mismatch with code intent). Highlight suspicious lines and suggest rewrite examples that match the code's function."

Bulk CSV audit

For editorial queues and moderation pipelines

Prompt template: "Given a batch of text entries (CSV column), return a compact CSV with columns: original id, verdict (Likely human / Likely AI / Ambiguous), primary indicators, and a short reviewer note suitable for import into an editorial queue."

Research draft provenance

For academic and research editors

Prompt template: "Evaluate this research draft for signs of AI assistance. Note citation anomalies, abrupt topic shifts, and improbable synthesis. Mark sentences with provenance risk and suggest how to verify sources."

Where teams run checks

Use cases and ecosystems

The detector is designed to fit into common content ecosystems. Use it for spot checks or as part of a staged review process before moving material into a learning management system, CMS, or applicant-tracking workflow.

Learning management systems: quick instructor checks for essays in Canvas, Moodle exports, or pasted text
Content management and editorial drafts: WordPress, Google Docs, and Markdown workflows
Recruitment and HR: screening application text or cover letters before manual review
Code reviews and repositories: audit commit messages and inline comments from GitHub/GitLab
Moderation queues: pre-screen forum posts or comments before escalating

Minimize exposure and retention

Privacy and sensitive content

For sensitive or proprietary text, follow privacy-first guidance: run checks in the browser when available, redact personally identifiable information before submission, and avoid bulk uploads of confidential records. The free detector is intended for quick, one-off checks—use on-prem or enterprise solutions for formal audit trails and longer retention controls.

Run short samples or redacted excerpts for sensitive materials
Keep only export-ready summaries in your review queue, not raw text
Use bulk CSV templates to control what fields leave your environment

Actionable outputs, not final judgments

Interpreting results and next steps

A 'Likely AI' result identifies patterns consistent with machine generation, but it should inform—not replace—human judgement. Use the highlights and indicator list to guide verification, request source work, run plagiarism checks, or ask for resubmission with a focus on original examples.

Likely AI — strong pattern matches; prioritize human review and verification
Ambiguous — mixed signals; combine with plagiarism checks and context review
Likely human — no strong indicators; still consider context (e.g., heavy editing)

From one-off checks to review queues

Bulk checks and export formats

For larger workloads, prepare a CSV with one column of text and an identifier column. Use the bulk CSV prompt template to receive a compact CSV with a verdict, primary indicators, and a reviewer note that you can import into editorial or compliance queues.

Supported export formats: plain-text verdicts and CSV summaries for queue imports
Use reviewer notes to preserve context (submission ID, source URL, excerpt)
Design a two-step workflow: detector → plagiarism tool → human adjudication

Know what detection can and cannot do

Limitations and model changes

Detectors look for statistical and linguistic patterns, which change as language models evolve. New models, fine-tuned systems, or human edits can reduce detection confidence. Treat outputs as advisory: useful for triage and prioritization, not as sole evidence in disciplinary or legal actions.

Detection confidence can vary with text length, genre, and editing
Highly edited AI text or short excerpts may produce ambiguous results
Combine indicators with human review and external verification for high-stakes decisions

FAQ

How does the detector decide whether text is AI-written and what do the verdict labels mean?

The detector analyzes linguistic and structural signals—such as repetitive phrasing, unnatural transitions, and citation anomalies—and maps them to qualitative indicators. Verdict labels are simplified summaries: 'Likely AI' (strong indicator patterns), 'Ambiguous' (mixed or weak signals), and 'Likely human' (no strong AI-like patterns). Each verdict is accompanied by highlighted sentences and an indicator list to help reviewers understand the reasoning.

Can the detector distinguish between AI-assisted editing and fully AI-generated text?

The tool flags linguistic patterns rather than provenance categories. It can surface edits that look machine-like (e.g., sudden stylistic consistency or generic phrasing), but it cannot reliably prove whether a human or an AI produced a specific phrase. Use the indicator list and highlighted sentences to decide if additional verification—such as asking the author about process or requesting drafts—is warranted.

What steps reduce false positives when scanning student essays or applicant materials?

To reduce false positives: scan longer excerpts rather than single sentences, remove assignment prompts or rubric text before analysis, consider domain-specific language that looks repetitive, and combine detector output with plagiarism checks and instructor knowledge of student voice. Where feasible, review flagged sentences in context instead of relying solely on the verdict label.

Does the tool store submitted text or keep logs that could expose sensitive content?

The free browser-accessible detector is optimized for quick checks and encourages redacting sensitive details before submission. For workflows that require strict retention controls, use enterprise or on-premise solutions that support audit logs and configurable retention. Do not submit highly sensitive or proprietary content unless you have appropriate retention and privacy guarantees in place.

How should publishers interpret an 'ambiguous' or low-confidence result in editorial workflow?

Treat 'ambiguous' as a prompt for additional checks: review the flagged passages, run subject-matter checks (original examples, sources), and consider asking the author for drafts or a revision with added attribution or context. Use the remediation checklist from the SEO article prompt template to improve originality and publisher-readiness.

What file types and batch sizes are supported for bulk checks and exports?

The free detector supports pasted text and compact CSV inputs for batch checks (one text column plus an identifier). For larger-scale processing, export-ready CSV templates are recommended: include an ID column and one text column. If you need enterprise-scale ingestion or larger file-type support, consider integrating a managed solution or contacting your platform provider.

Are there recommended workflows for combining this detector with plagiarism and human review?

Yes. A practical workflow: (1) Run the detector to triage and flag suspect submissions, (2) run plagiarism and source-verification checks on flagged items, (3) compile a short reviewer note and escalate to a human reviewer for adjudication, and (4) document the outcome in your LMS or editorial queue. Exportable CSV summaries speed steps 2–4.

What are the known limitations when detecting outputs from the latest large language models?

Detection relies on identifying patterns that may shift as models improve or are fine-tuned. Highly edited AI outputs, hybrid human–AI drafts, or very short excerpts may produce low-confidence or ambiguous results. Regularly update your review playbook and combine detector outputs with human expertise for high-stakes decisions.

How can teams integrate detector checks into a review workflow without disrupting UX?

Keep the detector as a quick triage step: use copyable verdicts and CSV exports to append results to your existing editorial or compliance queues. Provide reviewers with the highlighted sentences and a short reviewer note to minimize context-switching. For higher-volume needs, create a CSV ingestion step that returns a compact summary back into your workflow.

Is the detector suitable as evidence in disciplinary or legal proceedings?

No detector should be used as sole proof in legal or disciplinary contexts. Results are advisory: they identify linguistic patterns consistent with AI generation but do not establish intent or provenance. For formal proceedings, combine detector output with corroborating evidence, human adjudication, and legal counsel.

PricingCompare paid plans for enterprise detection and retention controls.
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Blog: Best practicesRead guides on combining detection with editorial and academic review.
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IndustriesExplore sample workflows for education, publishing, and compliance.