AI Content Detector
Analyze a sufficiently long sample for statistical patterns associated with AI-generated writing, then review the strongest sentence-level signals. The score is an estimate to interpret in context—not proof of who wrote the text.
The result is an estimate for careful review—not proof of authorship or misconduct.
No AI detector is 100% accurate. Do not use a score by itself to accuse, penalise or identify a writer.
Use an AI-detection score as context, never a verdict.
AI-writing detection is probabilistic. VerbaSpaces presents likelihood estimates and sentence-level signals, but authorship cannot be established from a detector score alone.
What AI detection can and cannot determine
An AI detector can identify statistical patterns that resemble writing produced by language models. It cannot prove who wrote a passage, how it was edited or whether permitted tools were used during the writing process.
How the detector works
At a high level, the VerbaSpaces AI content detector sends an eligible text sample to the existing detection service and displays AI-likelihood, human-likelihood and sentence-level signals returned by that service.
How to analyze text
Paste at least 300 characters; 600 or more usually provides a more informative sample. Read the overall estimate, inspect the strongest sentence signals and consider the text’s language, genre and editing history.
Why the score is not definitive proof
Human writing can receive a high score, and AI-assisted writing can receive a low one. Formal prose, translation, repeated sentence patterns and short samples can all affect an AI text detector.
Academic-use considerations
An academic AI detector should support a fair review process rather than automatic accusations. Course rules, drafts, citations, version history and a conversation with the writer provide evidence a score cannot supply.
A score is a prompt for review
Imagine a detector returns a high AI-likelihood estimate for a formal academic paragraph.
The passage contains patterns the detection model associates with AI-generated text.
The score does not establish authorship, misconduct or which tools were used to produce the passage.
Use the tool. Keep the context.
AI detectors can misclassify edited, translated, academic and non-native writing. The result should never be treated as proof of authorship.
AI and human likelihood estimates
The result stays connected to the writing, research and revision workflow.
Sentence-level signals
The result stays connected to the writing, research and revision workflow.
Clear limits and reliability warning
The result stays connected to the writing, research and revision workflow.
What to know before you use it.
Is an AI-detector percentage proof?+
No. It is a statistical estimate and should not be used alone to accuse or penalise a writer.
How much text should I provide?+
The tool requires at least 300 characters, while 600 or more usually provides a stronger sample.
Can human writing receive a high AI score?+
Yes. Formal, repetitive, translated, edited or non-native writing can be misclassified, which is why the surrounding evidence and writing process matter.
Can AI-generated text receive a low score?+
Yes. Editing, short samples and changes in model behaviour can affect detection signals. No AI text detector can determine authorship with certainty.
How should universities use AI-detection results?+
A score should prompt careful review, not an automatic allegation or penalty. Institutional policy, drafts, references and a discussion with the writer provide essential context.
One intelligent workspace.
Understand the task. Research smarter. Write in your voice. Cite accurately. Submit with confidence.
Analyse AI likelihood →