Funding AI education is 2 of 7 total priorities, divided into two $25M funds, with grants ranging from $1-4M for a 4-year project term.

1. The "Advancing AI to Improve Educational Outcomes of Postsecondary Students" priority will support projects that use AI to enhance teaching, learning, and student success in education.
2. The "Ensuring Future Educators and Students Have Foundational Exposure to AI and Computer Science" priority will support projects that broaden access to AI and expand computer science course offerings. At first, I thought all this money was for only for postsecondary goals, but priority 2.f on page 15 says, "Partner with SEAs and/or LEAs to provide resources to K-12 students in foundational computer science and AI literacy, including through professional development for educators." Eligible applicants: Institutions of higher education, consortia of such institutions, and other public and private nonprofit institutions and agencies. The Department expects to make awards by December 31, 2025

If we take learning to be a durable change in long-term memory and if we take instruction as the key lever of that and if AI can teach better than humans, not as some distant possibility but as an emerging reality, then we must reckon with what that reveals about teaching itself.

The lesson here is not that AI has discovered a new kind of learning, but that it has finally begun to exploit the one we already understand.

But let’s be clear. Again, the history of Edtech is a story of failure, very expensive failure. This is not merely a chronicle of wasted resources, though the financial cost has been considerable. More troubling is the opportunity cost: the reforms not pursued, the teacher training not funded, the evidence-based interventions not scaled because capital and attention were directed toward shiny technological solutions. As Larry Cuban documented in his work on educational technology, we have repeatedly mistaken the novelty of the medium for the substance of the pedagogy.

The reasons for these failures are instructive. Many EdTech interventions have been solutions in search of problems, designed by technologists with limited understanding of how learning actually occurs. They have prioritised engagement over mastery, confusing students’ enjoyment of a platform with their acquisition of knowledge. They have ignored decades of cognitive science research in favour of intuitive but ineffective approaches. They have failed to account for implementation challenges, teacher training requirements, and the messy realities of classroom practice.