The Tool Desk
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What AI detection actually means
An AI-text detector classifies submitted text. Its model has been trained or designed to distinguish examples written by people from examples produced by language models, and it estimates which class a new passage resembles. The output can be a percentage, a label such as “likely AI-written,” or highlights showing passages the system considers suspicious.
That is different from proving origin. The software normally sees only the final text; it does not know who typed it, which drafts existed, whether a person edited an AI suggestion, or whether a writer used translation or grammar software. A high score therefore means “this text matches patterns in the detector’s reference data,” not “an AI wrote this text.”
Classifier versus provenance
A classifier infers likely origin from wording. Provenance systems attempt to carry origin information through metadata, a cryptographic record or an embedded watermark. Those are separate approaches. Metadata can be stripped when a file is copied, and transformations can weaken a watermark. Conversely, the absence of a provenance signal does not establish that a person wrote the text.
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OpenAI has discussed signed metadata and text-watermarking research, including the possibility that watermark false positives could accumulate when a signal is used at very large scale. Provenance can support an investigation, but it is not a universal authorship certificate.
How a detector produces a score
- Input filtering: The service extracts text and may ignore or separately handle code, lists, tables, citations and other material that is not ordinary prose.
- Feature analysis: A model examines patterns such as word choice, sentence construction and predictability. The exact features and weighting differ by vendor.
- Classification: The system compares the passage with patterns learned from human and generated examples.
- Reporting: It returns a score, category and sometimes highlighted regions. Thresholds determine whether a low-confidence result is shown precisely, suppressed or marked as uncertain.
OpenAI’s 2023 experimental classifier illustrates one possible method: a language model was fine-tuned on paired human and AI responses to the same prompts. OpenAI divided examples into prompts and responses, generated model answers for those prompts and adjusted the public confidence threshold to reduce false positives. That description applies to that retired classifier, not to every current product.
Turnitin describes its AI Writing Report as identifying qualifying prose that its model judges could have been generated by a large language model, or generated and then modified by an AI paraphraser or bypasser. Turnitin calls the method complex and keeps its AI percentage separate from its similarity score. Similarity measures overlap with existing sources; AI detection estimates whether writing resembles generated text.
Can an AI detector prove authorship?
No. Both false positives (human writing labeled as AI) and false negatives (AI writing missed) occur. OpenAI discontinued its experimental classifier on July 20, 2023, citing low accuracy. In the challenge set OpenAI reported, it marked 26% of AI-written English text as “likely AI-written” and incorrectly marked 9% of human-written English text. Those figures describe that test and product; they are not a universal error rate.
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Why results vary
Length and content type
Longer prose gives a model more evidence. OpenAI described its own classifier as very unreliable below 1,000 characters and unreliable on code. That threshold must not be generalized to other detectors. Turnitin’s current report requires at least 300 words of long-form prose, accepts up to 30,000 words and files below 100 MB, and does not reliably evaluate poetry, scripts, code, bullet points, tables or annotated bibliographies.
Language coverage
OpenAI reported substantially worse performance outside English. Turnitin lists English, Spanish, Japanese and Arabic for its AI Writing Report, with language-specific behavior. Its English detector includes AI-paraphrasing and bypasser detection; the Spanish and Japanese versions do not, according to its guide. A score in a supported language is not automatically comparable with a score in another language.
Editing and predictable writing
Paraphrasing, translation, grammar correction and ordinary revision can alter the signals a detector uses. Highly formulaic human writing may look machine-like, while lightly edited generated text may evade detection. The 2023 study Testing of Detection Tools for AI-Generated Text evaluated 12 public tools and two commercial systems and concluded that the tested tools were not accurate or reliable overall; obfuscation made results worse. It is historical evidence about that evaluation, not a current leaderboard.
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Turnitin’s guide says results above 0% and below 20% are not displayed as a precise percentage; an asterisk identifies that less reliable range. It attributes a higher incidence of false positives there. Reports generated before July 8, 2024 may show a numeric value under 20%. This is Turnitin policy, not a general cutoff for AI detection.
What an AI detection score means in practice
| Output | Reasonable interpretation | What it cannot establish |
|---|---|---|
| Low or no score | The passage did not strongly match the product’s learned patterns. | That a human wrote every sentence. |
| High score | The passage resembles generated examples under that product’s settings. | Who authored it, which model was used, or whether a policy was broken. |
| Highlighted spans | Specific wording the system considered more machine-like. | That each highlighted sentence was generated. |
| No score or asterisk | The text may be too short, outside supported content or in a low-confidence range. | That the text is human-authored. |
Never compare percentages from different products as if they were the same measurement. Each vendor uses its own training data, threshold, supported languages and reporting rules.
How to evaluate a flagged passage fairly
- Check eligibility. Confirm the language, word count, file size and content type meet the named product’s requirements.
- Read the highlighted text. Look for ordinary explanations such as a template, technical style, translation or heavy editing. Do not treat highlights as a confession.
- Review process evidence. Drafts, revision history, notes, source records and a discussion with the writer can reveal how the work was produced.
- Apply the relevant policy. In education, follow the institution’s published rules and give the writer an opportunity to respond.
- Document uncertainty. Record the product, report date, language, threshold display and the other evidence considered.
OpenAI’s educator guidance recommends constructive approaches such as asking students to share relevant AI conversations, retain source records and explain how they evaluated AI output. An AI system’s own answer about whether it wrote an essay is not verification; ChatGPT has no knowledge establishing authorship of a submitted text.
Using screenshots as an audit record
If a detector report is part of a review, preserve the original export and access details under your organization’s privacy rules. A screenshot can document what a reviewer saw at a particular time, but it does not make the underlying score more accurate. Avoid uploading confidential student work to an unrelated service, and redact personal information when policy requires it.
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Troubleshooting detector results
The report gives no percentage
Check whether the product suppresses low-confidence values, whether the passage is under its minimum length, or whether it contains unsupported material. Turnitin’s asterisked sub-20% range is intentionally not a precise percentage.
A human-written passage was flagged
Review language, length, formulaic phrasing, translation and editing history. Preserve drafts and ask the writer to explain the process; do not impose a penalty from the score alone.
Generated text was not flagged
That is a false negative, not proof of human authorship. Editing, paraphrasing, short input and unsupported formats can reduce detectable signals.
Two tools disagree
Compare their language and content requirements, thresholds and definitions of “AI-written.” Their percentages are not interchangeable. For consequential cases, follow policy and weigh process evidence instead of selecting the more alarming result.
A screenshot is blank or incomplete
For a capture record, wait for a selector or network idle, load the page at full length, and check the response’s X-Page-Verdict and X-Billed headers. A failed load or blank page is not billed by ScreenshotNeo, but you should still retain the detector’s original report.
Do AI detectors work?
They can identify patterns worth investigating, but current evidence does not support treating any detector as a universal authorship test. OpenAI’s retired classifier had low accuracy, and a 2023 multi-tool evaluation found broad reliability problems. Product documentation, language, text type, threshold and model version all matter. The defensible use is exploratory: combine a report with drafts, sources, revision history, conversation and fair human review.
Frequently Asked Questions
Is an AI score the same as a plagiarism score?
No. AI detection estimates whether wording resembles generated text, while a similarity report measures overlap with existing sources.
Can I use a detector on code or bullet points?
Check the named product’s rules first. OpenAI’s retired classifier was unreliable on code, and Turnitin says code and bullet points are not reliably detected as qualifying prose.
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Should a school punish a student based only on a detector?
No. Turnitin says its report may misidentify human and AI text and should not be the sole basis for adverse action; human judgment and institutional policy are required.
Does provenance metadata prove a person wrote text?
No. It can provide origin information when intact, but copying or transformation may remove it, and its absence does not prove human authorship.
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