Can AI Text Detectors Be Trusted? Accuracy & Limits
AI text detectors are widely used in classrooms and newsrooms, but they are far less reliable than image provenance checks. Understanding why helps you use them responsibly.
How text detectors work
Unlike images, text carries no metadata saying which model wrote it. Detectors instead look at statistical patterns: how predictable each word is, how much sentence length varies (burstiness), vocabulary range, and phrases that language models overuse.
Why false positives happen
- Formulaic writing: legal text, lab reports and five-paragraph essays are naturally uniform.
- Non-native English: simpler vocabulary and careful grammar can resemble model output.
- Grammar tools: heavy use of editing assistants smooths text toward AI-like patterns.
Why false negatives happen
Light human editing, paraphrasing tools or prompting a model to "write casually" can push AI text below detection thresholds.
Best practice for teachers and editors
- Treat a score as a reason to look closer, never as a verdict.
- Compare with the person's earlier writing.
- Ask for drafts, notes or version history.
- Have a conversation about the content; authors can discuss their own reasoning.
Try our AI text detector. It shows exactly which signals drove the score so you can judge for yourself.