How AI detectors work and how to navigate them: detection signals, false positive risks, and what the scores mean for academic writers.
39 articles · Page 5 of 7
Yes, Turnitin detects paraphrased text, and it was built to. Its similarity engine catches reworded passages that still overlap with published and previously submitted work, and its AI writing report carries a labelled category for AI-generated text revised with a paraphrasing tool or word spinner. Here is how each system catches it, and why swapping words for synonyms clears neither one.
Turnitin's AI writing detector does not read a document as a whole. It segments a submission into chunks, scores each one, averages the results, and only then produces the percentage on a report. This covers that pipeline, what it flags across ChatGPT, Claude and Gemini, what independent testing found it misses, and who actually gets to see the number.
Turnitin publishes no acceptable similarity percentage, and institutions set their own guidance instead. This post explains what actually drives a high or low score, why quoted material and reference lists inflate it without anything being wrong, and how to read the match breakdown behind the number rather than the number itself.
A similarity score and an AI score answer different questions, so a paper can score low on one and high on the other without either number being wrong. What each measures, what triggers it, and what a high number does and does not prove.
Raising perplexity and burstiness on purpose usually just means writing better: varying sentence length, mixing clause structures, and choosing the word that actually fits instead of the one that merely scans. Concrete before-and-after examples, plus where this stops being craft and starts being a bad idea.
GPTZero made perplexity and burstiness famous, then quietly retired both. Here is what its classifier measures now, what a report actually shows, and where the tool says its own limits are.