When will we have better research on AI in education?
O’Neill’s findings also raise ongoing problems about the strength of peer review, a quality-control mechanism that is supposed to catch problems before publication.
“Given the number of serious errors that should have been caught, or at least cause for a correction and probably in some cases weren’t reparable, this is already a serious problem,” he said. “They’re at 11 different journals that are very highly reputed. These just should not have gotten through.”
It’s not clear when educators will have access to better big-picture answers on AI education, in part because it takes a while to generate enough high-quality studies, even as use of the technology accelerates and evolves at a rapid clip. An explosion of new research is now beginning to be reviewed and sorted.
The AI Hub for Learning, part of Stanford University’s Accelerator for Learning research group, has been attempting to categorize the evidence on AI learning. In its 2026 review of more than 800 studies, just 20 of them had a strong cause-and-effect design, and not a single one involved K-12 students. The organization is now reviewing dozens of additional papers and hopes to release an update soon, said Susanna Loeb, a professor at Stanford, during the webinar.
Though it’s too early to gain insight from a meta-analysis, Loeb said she hopes the field can learn from the limited amount of high-quality research on AI in education.
“But it requires that each of us are really honest in what papers and research studies can do and what they can’t do, and that trusted voices can emerge in here,” she said.
