This week, I read an article asking a question that has been floating around staff rooms, conference halls, and dinner tables. All of which I’ve frequented recently, and am very much open to more invitations (especially for dinner tables). Here is the article, I highly recommend taking a look by Dr. Dirk Van Damme , with contributions from Charles Fadel , for the Center for Curriculum Redesign.
The piece explores a growing concern: as AI systems become more capable, humans may outsource not just calculation, but cognition. Not just execution, but judgment. The subtle fear is this:
If machines are increasingly doing the thinking…what happens to our thinking?
It’s a fair question. Though I believe the article and this discussion can reposition fear into pragmatic action.
Every generation has its “this will ruin thinking” moment. Writing and written language. The printing press. The calculator. The internet. Google. Now AI.
Dr. Van Damme’s article names something important here: hodiecentrism-- the tendency to believe that today is uniquely unprecedented, uniquely disruptive, uniquely perilous. It’s a quiet assumption that we are living through the most consequential shift in human cognition ever. Alongside hedoicentris, the author warns us against two familiar camps:
Techno-optimism- Technology will save us.
Techno-pessimism- Technology will ruin us.
Both are emotionally satisfying. Neither is particularly helpful. Because both assume the tool is the main character.
Mathematics teachers and other educators know better.
Tools have always changed. What matters is what humans are oriented toward while using them. The calculator did not destroy reasoning. It exposed whether classrooms were built on reasoning or repetition. AI will do the same. Here is how I interpret the exposure that is coming or already here:
If we are complacent with computation, AI outperforms us.
If we are in doubt about instruction, we will seek better tools and programs.
If we change the metaphor for knowledge and learning to one more akin to what is being exposed by AI, we redefine the purpose of education and mathematics.
If this transformation occurs, mathematics will be recognized for what it has always been: a creative, human language for interpreting and shaping the world. To sustain this, we need recognition of the talent our classrooms need and of the fact that current teacher prep falls way short.
The danger is not AI. The danger is hodiecentrism, convincing us that this moment is about machines rather than orientation. Technology amplifies what we optimize for. The question isn’t whether AI enhances or diminishes human intelligence.
The question is:
What kind of intelligence are we cultivating in the first place?
In my reading, Dr. Van Damme’s work encourages balance: Technology enhances some capacities and diminishes others. It increases speed. It increases access. It may decrease certain forms of memorization or recall.
But here’s the deeper question for schools:
Have we trained students to retrieve answers — or to reason through structure?
If schooling is optimized for retrieval, AI will outperform us. If schooling optimizes for modeling, representation, transfer, sensemaking, conceptual coherence, and identity development, then we are building skills that go beyond output generation.
The good news is that this is entirely possible within the K-12 and Higher Education system we have created over the last 150 years. We need some emergent processes to evolve that tool.
In the emergent transformation progression, we describe four stages that education organizations move through, which are an implementation of what is described in the article:
Complacent with Computation: Mathematics is treated primarily as the execution of procedures, where success is measured by accuracy and speed rather than by understanding. Tools and AI dramatically outperform humans at the optimized layer (procedure), thereby exposing the model's fragility.
Instructional Doubt: Strong implementation persists, but outcomes remain flat, leading educators to question strategies without yet redefining mathematics itself. Juggling a new curriculum and tools has not proven effective in increasing student learning. Hodiecentrism emerges — we assume this moment is unprecedented, rather than noticing misalignment across layers.
Metaphor Change: Mathematics is reframed as a sensemaking discipline, with reasoning, representation, and student thinking central. AI becomes a cognitive partner — something to interrogate, critique, and refine.
Instructional Transformation: Mathematics becomes a creative, human language for interpreting and shaping the world, and systems align to protect and sustain that orientation. Talent, talent creation, and coordination between people, tools, and processes become standard practice. AI is placed within a coherent system — enhancing without displacing human orientation.
The article reminds us that technological change operates across five interacting layers:
Cognitive (how individuals think)
Task / Practice (what learners are asked to do)
Tool / Technology (what mediates thinking)
Institutional (structures, incentives, policies)
Cultural (shared beliefs about intelligence and knowledge)
Technology primarily shifts the tool layer. MathTrack Institute‘s GROWTH Framework operates across all five.
Cognitive Layer- How Thinking Develops: The first couplet of GROWTH is Grasp the Meaning, and Reveal the Horizons. In a storytelling and narrative framework, this means that students (and teachers) surface prior understanding and articulate reasoning. Errors become cognitive data, not deficits. GROWTH shifts cognition from recall to structure-seeking. AI may generate an answer. GROWTH trains humans to analyze the structure beneath it.
Task / Practice Layer — What Students Actually Do: The second couplet of GROWTH is Observe the Implications and Weave Together Narratives. In a storytelling and narrative framework, this means that teachers become aware of the language they use to tell the story of mathematics and co-create learning environments with students that privilege making sense over producing answers. AI becomes material for analysis rather than endpoint production.
Tool/Technology Layer — How Tools Are Positioned: The Teach with Peers GROWTH layer focuses on increasing interactions between teachers and tools. Technology is explicitly framed as a modeling and instructional instrument. As teachers learn to interrogate tools to facilitate their storytelling, students learn to interrogate AI, adjust assumptions, test sensitivity, and compare human to machine reasoning. AI is placed within the reasoning loop, but it does not replace it.
Institutional Layer — How Systems Reinforce Orientation: The Hear your Students GROWTH layer focuses on increasing interactions between teachers, leaders, tools, and students. Leaders and teams study artifacts of student reasoning, not just test performance or computation precision. Professional learning becomes collective noticing, analysis of student work, and shared language development across grade bands. Technology and tools become coherent within this level of interaction rather than chaotic.
Cultural Layer — What We Believe Mathematics Is For: This is all six GROWTH movements together. When there is a GROWTH transformation, mathematics is no longer a delivery system, gatekeeping filter, or compliance structure. Mathematics is redefined in the interactions and activity of the learning environment as a creative human language for interpreting and shaping the world. That cultural shift stabilizes the other four layers.
The Center for Curriculum Redesign article urges balance. Not techno-optimism. Not techno-pessimism. But disciplined attention to how tools reshape cognition across layers. GROWTH is our disciplined response. It does not panic. It does not worship. It studies.
It asks:
What is being enhanced? What is being diminished? What must be protected? What must evolve?
Hodiecentrism tempts us to believe this moment is unlike any other. History suggests otherwise. What is different is the speed. Which means clarity of orientation matters more than ever. Because when the pace accelerates, confusion scales faster. But so does coherence.
If AI can write, solve, summarize, predict, and generate, then education cannot define intelligence solely in terms of production. That game is over, and I’m thankful it isn’t going into overtime. The future belongs to those who can interrogate structure, compare representations, model uncertainty, revise assumptions, and construct meaning within a community. Those are not nostalgic skills. They are evolutionary ones. And as I have often shared, they have always been at the heart of mathematics —when mathematics is understood as the creative force it is rather than a procedural filter.
If we are honest, much of schooling has been optimized for what machines do well now. Speed. Recall. Compliance. Coverage. AI is not breaking education. It is revealing it. And that revelation is uncomfortable. But it is also generous because it gives us a chance — deliberately — to decide what kind of intelligence we want to cultivate next.
In districts and schools where we are doing this work, something interesting happens. Teachers stop asking, “What tool should we adopt?” And start asking, “What is mathematics for in our community?” Leaders stop asking, “How do we raise scores quickly?” And start asking, “What orientation are we protecting over time?” Students stop asking, “Is this right?” And start asking, “Does this make sense?”
That is not small. That is cultural. And culture is more durable than software.
So no, I don’t believe technology is making us dumber. I believe it is clarifying. Clarifying whether we were training execution or cultivating understanding. Clarifying whether we saw mathematics as a delivery or a language. Clarifying whether teachers are implementers or cultural stewards. AI is a powerful tool. But it is still a tool.
The real transformation is human.
And if we align cognition, tasks, tools, institutions, and culture — if we stabilize the metaphor — then AI will not diminish intelligence. It will amplify it. I choose not to ask the question of whether machines will think for us, but rather, will we think more deeply because they exist? I’m betting yes. Because every time I sit in a room where teachers are studying student reasoning together — slowly, carefully, curiously — I see something no algorithm replicates:
Intellectual humility. Shared meaning-making. Storytelling with structure.
That is not obsolete. That is the future.