Responsible AI adoption in education requires two things. They are not in tension. They are not alternatives. They are both necessary, and neither is sufficient without the other.

The first is pedagogy. Does the tool strengthen the student-teacher-content relationship or weaken it? Does it support the cognitive work that produces learning, or does it displace that work? Is it deployed as part of a deliberate instructional design, or is it dropped into practice and left to operate without evidence of what it actually does? The field's most serious institutions — Brookings, UNESCO, UNICEF — have spent significant resources trying to answer these questions, and the answers are worth taking seriously. AI enriches learning only under specific conditions. Those conditions require deliberate design.

The second is governance. Who controls the environment in which pedagogy occurs? What has the institution agreed to in the contract before the tool was ever used in a classroom? Whose data is being collected, under what terms, stored where, and used for what purposes? Does the institution retain the capacity to change direction, or has it ceded that capacity to a vendor without realizing it?

These are equally the right questions. The field has not been asking them at anything close to the same scale.

No editorially independent publication in education connects the research community to itself and to the practitioners whose work it is supposed to inform — across formats, across disagreements, and including critical coverage of its own structural conditions.

Neuroscience built that infrastructure when it created The Transmitter and its recurring series "How to Teach This Paper" — a model for walking practitioners through not just what a study found, but how it was designed, what the methods assume, and what it would take to apply it responsibly. Education has not.

The problem is not the absence of data. It is the inability of systems to turn evidence into instructional improvement. Two reports. Two contexts. One structural failure.

This week in UNBOUND, a study of data storytelling in U.S. data science classrooms and a policy brief on foundational learning data in Kenya reveal the same challenge: education systems often generate evidence but fail to translate it into changes in teaching.