Education, When AI Just Is
The 10 Questions at the Heart of What Education Becomes
The Patrick Dempsey: #0126
I write weekly articles about possibility, reinvention, and the courage to begin something new.
What Education Becomes
Here’s the question at the foundation of What Education Becomes: Teaching and Learning in a Post-AI World.
What does education look like when AI just is?
At the time of its release on February 17, 2026, I felt the ideas in What Education Becomes were well ahead of their time.
We wanted to be the first talking about these ideas.
When I came up with the idea for the book in the summer of 2025, I was imagining a world of AI ubiquity sometime around 2030. The goal was to help individuals and institutions start thinking big about what was coming.
And so, we put together ten chapters by 12 contributors on the possibilities and challenges of AI in education as we saw them at the time.
Since the launch of the book, AI has already become ubiquitous.
AI is embedded in our browsers, our phones, our search engines, our workplaces, and increasingly the ordinary applications through which we read, write, communicate, and make decisions. We are quickly moving beyond a world in which people consciously decide to “use AI.” It is becoming part of the environment in which cognition happens.
With that, also, the model capabilities have moved well beyond what they were even 5 months ago when the book came out.
And so, education, it seems, is still catching up. But it does appear as if the time for wait-and-see has passed. Institutions know they need to do something about AI.
That makes the questions we ask in our book more relevant with each passing day.
The Questions We Asked
Across three sections and ten chapters, the contributors explore what education might become once we stop treating AI as a temporary disruption or an optional classroom tool.
The first section reconceptualizes education’s foundations: curriculum, credentialing, institutional structures, and the relationship between learning and work.
The second examines what must be protected as AI removes friction and expands optimization: purpose, psychological boundaries, productive struggle, uncertainty, and the risks through which genuine learning occurs.
The third turns toward teaching and learning itself: how educators might design for integrity, literacy, judgment, human agency, and educational abundance in a world where AI is always present.
Thankfully and purposefully, none of us claim to predict the future. Our more modest goal was simply to imagine education in a world where AI just is.
So rather than a playbook of prompts, action items, and next steps, each chapter is driven by questions rather than solutions.
These are my ten favorites.
10. What if learning happened through participation?
What if learning no longer prepared people to participate in consequential work, but occurred through that participation itself? And if human interpretation governs increasingly intelligent civic systems, does the state inherit an obligation to build learning infrastructure as deliberately as it builds technological infrastructure?
Krystal Rawls and Haley Miguel imagine a Civic Institute in which professional development, public work, assessment, and civic accountability become part of the same system.
Learning is no longer something that happens before meaningful participation. It emerges through participation itself.
9. What evidence of learning remains?
If AI can produce the artifact, what evidence of learning remains? How do we move from verifying that students worked independently to revealing how they exercised judgment, agency, and integrity while working with intelligence that was not their own?
Craig Seal asks us to move beyond an understanding of academic integrity organized around independence.
When students inevitably work with AI, the important question is no longer whether they completed every part of the process alone. It is whether their judgment, decisions, reasoning, and agency can be made visible.
8. Is refusing to teach AI really protection?
When AI is already part of students’ literacy environment, is refusing to teach it an act of protection—or an abdication of teaching?
Susan Ray argues that silence is not neutrality.
Students are already reading, writing, thinking, and communicating within environments shaped by AI. Refusing to address that reality does not preserve some pre-AI form of literacy. It leaves students alone to navigate systems they have never been taught to question.
7. How do we know when affirmation replaces truth?
How do I know when I am seeking validation rather than truth? How do I maintain multiple perspectives when one explanation feels overwhelmingly compelling? And what happens when the intelligence I ask to reality-check my thinking is the same intelligence helping me construct the reality I am trying to check?
Anastasia Goudy, M.Ed. examines what happens when an AI system becomes more than an instrument.
It remembers. It accommodates. It mirrors our language and assumptions back to us. It helps us create a narrative and then becomes the source we consult to determine whether that narrative is true.
What happens when the mirror begins validating its own reflection?
6. What happens when struggle becomes optional?
What happens when AI makes productive struggle optional—or even invisible? If friction is necessary for knowledge to endure and for people to care about their work, what do we lose when intelligent systems remove it before we have even chosen whether we need it?
Jason Gulya explores a tension that education cannot avoid.
AI is increasingly designed to anticipate difficulty and remove it before we experience it. But learning science tells us that effort, uncertainty, revision, and struggle are often the very things that allow knowledge to endure.
The danger is not simply that students will avoid doing work.
It is that they may experience the feeling of mastery without undergoing the process through which mastery develops.
5. Which risks should education preserve?
Which risks does this system introduce? Which risks does learning require? How do we negotiate both?
Rachel Horst and Arafeh Karimi complicate the language of safety.
Some risks harm students and must be prevented. But learning itself also requires risk: the risk of being wrong, of remaining uncertain, of encountering disagreement, of struggling to find language for an idea that has not fully formed.
A system designed to eliminate every risk may also eliminate the conditions under which learning becomes possible.
4. Are our systems serving people—or training people to serve systems?
Are we building systems that honor people, or systems that people must conform to?
Jessica Maddry calls the destruction of purpose through excessive optimization refinicide.
Educational systems begin with good intentions: equity, accountability, access, improvement, and support. But each layer of refinement can create another process, metric, requirement, or mechanism of control.
Eventually, the system may become extremely effective at producing outcomes that no longer resemble its original purpose.
The question is not merely whether a system works.
It is whom the system requires people to become in order for it to work.
The three most controversial questions
The final three questions challenge some of education’s most foundational assumptions: what universities exist to verify, whether educational scarcity can still be morally defended, and whether the traditional fundamentals of schooling remain fundamental at all.
