Beth Anderson 8 min read · May 1, 2026
“AI has the potential to solve the last mile problem of putting learning science into practice.” – Melina Uncapher, SETA-ED
That line, heard at the ASU+GSV Summit in San Diego, has stayed with me more than anything else from the conference.
Not because it was the most provocative statement in a field full of bold claims about AI and education, but because it framed a problem that is much older than AI itself.
And returning from a recent set of school visits in London and Wales where Core Knowledge is implemented, it felt even more relevant.
These were schools where technology was not the centerpiece. AI was barely part of the conversation. In some classrooms, tech was absent entirely.
And yet, the focus on learning science, the “how” of learning rather than just the “what”, was unmistakable.
That contrast is important.
Because if AI is the accelerator, learning science is the engine. And in many places, the engine is still not consistently embedded in day-to-day practice.
The “last mile” problem is not new but still unsolved
In pockets across the system, the last mile problem has been addressed.
Not through software or platforms alone, but through people and practice:
- School leaders with clarity and conviction
- Educators deeply engaged with cognitive science
- Networks translating research into classroom reality
- Researchers insisting that “knowledge-rich curriculum” is not optional—but foundational
For example:
- Education leaders like Gareth Rein, who are intentionally building environments where curriculum, teacher development, and cognitive science reinforce one another in service of equity and attainment.
- Practitioner-trainers and translators like Meg Lee and Kristen McQuillan, who sit at the intersection of research and classroom implementation.
- Researchers like Tim Surdna and his team in Belgium, applying structured curriculum design at system level.
These efforts are not theoretical. They are operational. They are happening in real schools with real students.
But they are not yet consistent. Not sustained across systems. And not yet scaled in a way that makes them the default rather than the exception.
That is the gap Melina Uncapher is pointing to.
And it is where AI enters the story as a potential amplifier of what already works when it is grounded in learning science.
Three Schools, Three Contexts, One Clear Pattern
Recently, I visited three very different schools:
- St. Peter’s Primary School in Cardiff, Wales
- TASIS England in Surrey, England
- Michaela Community School in Wembley, England
Different intakes. Different models. Different leadership styles. Different cultures.
And yet the pattern across them was strikingly consistent.
What stood out was not just performance data or inspection outcomes, though all three are strong by relevant measures.
It was the students.
Students who speak with clarity and confidence.
Students who are eager to learn and able to articulate their thinking.
Students who take visible pride in what they know.
At St. Peter’s, one parent described the impact of leadership change in a way that has stayed with me:
“It has raised both the floor and the ceiling for all students.”
That is not a slogan. It is a description of what happens when expectations, curriculum, and teaching quality align.
Across all three schools, two things were present:
1. Leadership with clarity and conviction
Strong leadership is not just managerial. It is cultural.
It sets direction. It aligns adults. It makes expectations explicit. And it creates coherence across classrooms.
2. A knowledge-building curriculum, taught with precision
When curriculum is structured, sequenced, and intentionally knowledge-rich, and when teachers are supported in delivering it well, the effects are visible:
Students don’t just accumulate information. They build understanding.
And more importantly, they develop expectations about themselves:
- “I can actually understand this.”
- “I can explain this.”
- “I am expected to know this.”
That shift in expectation is one of the most powerful, and least discussed, drivers of educational equity.
Because once students expect success, the ceiling changes.
Not just for the highest attainers, but across the entire distribution.
The Four D’s of Knowledge-Rich Implementation
When people talk about what makes this kind of practice work, they often reach for frameworks.
One that came up repeatedly in conversation with Gareth Rein was what he jokingly called the “Four D’s” – four thinkers whose work underpins much of the current knowledge-rich curriculum movement.
Don (Dr. E.D. Hirsch, Founder of Core Knowledge)
E.D. Hirsch has spent decades arguing that knowledge is not incidental to literacy and equity, it is the foundation of both.
From Cultural Literacy to Why Knowledge Matters, his work reframed curriculum as a matter of justice, not preference.
Daniel Willingham
Daniel Willingham has done more than perhaps anyone else to translate cognitive science into usable insights for educators.
His core message is deceptively simple: thinking depends on knowledge. Memory is not a flaw in learning, it is the mechanism that enables it.
Daisy Christodoulou
Daisy Christodoulou has been one of the most influential voices in challenging misconceptions about assessment, skills, and “learning styles,” particularly in the UK context.
Her work returns to a central idea: we often overestimate the value of generic skills and underestimate the role of domain knowledge.
Doug Lemov
Doug Lemov brings something different but essential: practice.
Through thousands of classroom observations, his work focuses on what effective teaching actually looks like in action down to the routines, questions, and techniques that shape learning moment by moment.
What Actually Changes in These Schools
Across the schools I visited, what stood out most was not just what students knew. It was what they expected.
These young learners expected:
- to understand complex ideas
- to be held to high standards
- to engage seriously with challenging content
- to learn in an environment that is both structured and safe
That expectation changes classroom dynamics more than any single intervention.
Because once students believe that knowledge is something they are meant to access, not something reserved for a few, the entire system behaves differently.
Where This Leaves Us
The excitement around AI in education is real. So is the risk of overestimating what it can do in isolation.
What feels more grounded, and more urgent, is this:
The biggest constraint in education is not access to tools or information.
It is a consistent implementation of what we already know about how learning works.
Learning science is not new. Knowledge-rich curriculum is not new. Cognitive psychology is not new.
The challenge has always been the same: getting it into every classroom, every day, for every student.
If AI has a role worth paying attention to, it is not in replacing that work, but in helping close the gap between knowing and doing.
That “last mile” is still the frontier.