AI Text Detection Tool

Estimates the probability that a text sample was generated by AI, for editors, educators, and researchers needing a fast, data-driven indicator.

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About This Tool

The AI Text Detection Tool estimates the probability that a sample was produced by an AI model. It combines stylometric cues, lexical patterns, and model-probability signals to output a numeric score and a concise interpretation. The tool targets editors, teachers, content strategists, policy teams, and researchers who need fast screening for authenticity.
Conceptually, it analyzes input text, extracts features, and compares them against calibrated benchmarks. The score, ranging from 0 to 100, reflects AI-origin likelihood; higher scores indicate stronger AI signals. An accompanying confidence and brief notes explain the dominant contributing features and uncertainties.
What makes this tool unique is its clear articulation of probabilistic limits, immediate feedback, and exportable results for audits. Use cases include academic integrity checks, media moderation, policy compliance, and quality assurance in publishing. It is designed for rapid triage in editorial and classroom contexts, enabling data-informed decisions without relying on opaque heuristics.

How to Use

  1. Provide inputs: paste the text sample to analyze.
  2. Run analysis: the tool computes a probabilistic score and confidence.
  3. Review outputs: interpret the AI-likelihood score and the contributing features note.
  4. Apply context: weigh results alongside human judgment and case specifics.
  5. Export: save or export the result for records or audits.
How to use ai text detection tool free

Frequently Asked Questions

Find Quick Answers

What does the tool measure?
It estimates the likelihood that a given text was AI-generated by analyzing stylometric features, token distributions, and model-probability cues, returning a numeric score (0–100) and a concise interpretation.
Is the result definitive?
No. The output is probabilistic and designed to aid human judgment. It should be used as a signal within broader evaluation, not as a sole determiner of authorship.
What inputs are supported?
Plain text samples are analyzed; very long inputs may be truncated for performance. It supports samples in multiple languages and scripts, with results calibrated for general-use contexts.
How is data handled?
Inputs are used to compute results and are not retained for training by default. Users should refer to the platform's data-handling policy for persistence and sharing details.

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