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 6 of 7
Perplexity gets the attention, but the arithmetic underneath it is token probability: what the model's distribution over the next word actually contains, why detection math runs in log space instead of raw probability, and how a string of per-token scores becomes one number.
GPTZero made perplexity and burstiness famous, then quietly retired both in autumn 2023 for a deep-learning classifier. This post traces what changed, what GPTZero and Turnitin actually document today, and why the two statistics still explain why machine text is detectable even though neither vendor computes them directly anymore.
Getting caught is rarely a single moment triggered by a percentage score. It is a defined process with stages, evidence standards and appeal rights, most of which never make it past a first conversation. Here is what that process actually looks like end to end, using a published university procedure rather than a guess.
Detection software matters less here than most students assume. This walks through what a professor who has already read your work actually notices when a draft stops sounding like you, from a landmark blind study of examiners to the specific, checkable signs that show up long before anyone runs a scan.
A 2023 Stanford study found AI detectors misreading fluent non-native English writing as machine-generated at a much higher rate than native writing. Here is the mechanism behind that gap, and what happened when the same essays were rewritten with richer vocabulary.
Burstiness measures how widely sentence lengths swing across a passage, not how long the average sentence runs. This post explains where the term comes from, how the spread is actually calculated, and why a language model's output tends to settle into a narrow band of lengths.