I taught in a New York City high school for years. I loved the students. I loved the subject matter (English). What wore me down wasn’t any of that. It was the system. The relentless compliance. The evaluation rubrics that measured everything except whether I still felt alive in the work. The professional development that left me feeling like my time could have been better spent. The creeping sense that few people positions of power actually cared whether I was inspired, only whether I was performing.

So I left. Not the profession, but the classroom. I spent the years since trying to change the system: from the inside while working in the city’s Central offices and from the outside, as a researcher, a professor, a nonprofit leader. Somewhere in all of that, I landed on what I think is a central lesson of my career: Inspiration is more than a marketing ploy to lure people into teaching; it is the fuel that keeps teachers growing.

Seriously, Inspiration? Seriously.

That’s the idea behind the Inspiration Index—a tool we’ve been building at the Academy for Teachers, and the most ambitious effort I am aware of to put inspiration front and center in teacher development.

Here’s the problem the Inspiration Index is trying to solve, which might be described as a kind of a systemic bait and switch. Inspiration is what brings people into teaching. Ask any teacher why they chose this profession and you’ll hear some version of inspiration: a subject that lit them up, a student they believed in, a calling they couldn’t shake. But the moment teachers walk into a school building, inspiration disappears from the professional conversation entirely. No rubric measures it. No evaluation framework asks about it. No professional development session nurtures it. If we took inspiration more seriously, it might counterbalance what are disturbing trends in the profession.

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Nationally, 77 percent of teachers say their work is frequently stressful. Nearly 70 percent of early-career teachers have left or seriously considered leaving. In New York City, more than 40 percent of newly hired teachers are gone within five years. And teacher turnover here in the country’s largest district surpasses $200 million a year. That’s just the measurable part. The immeasurable part is the student who loses a trusted adult mid-year. The parent who has to start over with yet another new face. The colleagues left holding the bag in an already understaffed building.

Something fractures between the inspiration that brings people into teaching and the conditions waiting for them on the other side of the classroom door. The field has known this for years. What it hasn’t done is take inspiration seriously enough to actually measure it.

We Can Measure Inspiration

But some have. Psychologists Todd Thrash and Andrew Elliot established in 2003 that inspiration is a rigorous, measurable psychological state. Not a vague feeling, not a metaphor for enthusiasm, but a construct with distinct characteristics and documented consequences. Their research maps inspiration as a three-phase process I call: a Spark (something wakes you up to new possibility), an Insight (you see your work, your students, your purpose differently), and Action (you’re moved to try something, create something, stay).

The Academy for Teachers' Inspiration Index is built around that model, which we describe in a recent white paper. It combines a research-backed Matrix that maps our programming against what we know makes professional development actually work, a survey bank drawn from validated inspiration science and the NYC citywide teacher survey, and a single number that tracks the trajectory of a teacher’s inspiration over time.

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It is an attempt to build something the system never had but sorely needs: a rigorous, research-grounded way to detect inspiration in teachers, track it over time, and design professional learning that deliberately produces it. Not as a nice-to-have. As the whole point.

It’s time to move inspiration where it belongs, front and center to the head of the class.

My wife and I had an argument (or what some participants would call a “conversation”) over coffee this morning.

Not the kind that ends badly. The kind that happens when two people with doctorates in education, who have spent their careers inside schools and school systems, read the same news and land in slightly different places. New York City Public Schools recently announced it was suspending plans to open a high school dedicated entirely to artificial intelligence. That was the news. The argument was about what it means.

We kept circling the same question: not whether a single AI-dedicated high school was a good idea—we both agreed it probably wasn’t—but what the city is actually going to do about the reality of AI in schools. Because suspending one high school is not a strategy.

Here is what I know from twenty years of watching technology enter schools: it almost never does what its advocates promise.

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Thomas Edison believed educational film would revolutionize classrooms. It didn’t. The radio was supposed to transform learning. It didn’t. Television. The internet. Laptops. Bring Your Own Device movements. Each arrived with enormous promise and genuine enthusiasm from private sector partners, elected officials, and district leaders. Each time, the fundamental experience of school remained largely unchanged. Students still sit in rows. There’s still a teacher at the front of the room. The chalkboard became a whiteboard, and the whiteboard became a smart board. But the architecture of learning and teaching stayed the same.

I want to be careful here. AI is genuinely different in some important ways. The speed of its development, the breadth of its applications, the degree to which it can personalize interaction, these are real distinctions. I am not arguing that AI will have no effect on schools. I am arguing that the history of educational technology should make us deeply skeptical of any claim that it will overhaul them. That pattern of overpromising is not a coincidence. It is what happens when private sector interests, policy timelines, and genuine enthusiasm converge faster than pedagogy can keep up.

So what should the city actually do?

First, as I outlined previously, NYCPS needs to follow its initial AI guidelines with something substantive on pedagogy, and soon. The timeline they’ve set for themselves is ambitious. My strong suggestion: don’t treat this as an original composition. Treat it as a remix. There is already serious, rigorous thinking happening in other districts, states, nonprofits, and countries. Learn from it. Adapt it. The goal is good guidance for teachers, not authorship credit.

Second, and this is the idea I find most compelling, the city should consider a distributed AI labs model. We did something like this during the Bloomberg administration when we were exploring blended and online learning. Rather than designating one school as the AI school, identify AI lab sites across the system. For instance: one elementary, one middle, and one high school in every district across all five boroughs.

Each AI lab receives additional funding, which could be quickly raised in partnership with the city’s philanthropic partners. A meaningful portion of that goes toward buying time from a teacher who is already deep in this work, someone who has been genuinely experimenting with AI integration in their classroom. That teacher becomes the AI lab lead, working with a small cohort of colleagues to systematically explore what AI-informed teaching and learning can look like. They report back—both to central and at large convenings of lab leads across the city—building a shared knowledge base in real time.

For this to work, everyone has to do their homework. What is our theoretical framework? What have other cities and countries already learned? What are we strategically trying to find out, and what space are we leaving for genuine, unbridled experimentation? The answer to that last question, in my experience, should be: a little room for surprise, embedded inside a lot of intentional design.

This is not a new idea. It is a proven model applied to a new challenge. And it is infinitely more scalable, and more honest about what we know and don’t know, than concentrating all of our AI ambitions into a single school.

For nonprofits like the one I lead, there is a natural role here too. Organizations that specialize in teacher development, that have existing relationships with schools, that understand both the promise and the limits of ed-tech can plug into a model like this in ways that strengthen it. That requires reasonable funding and genuine strategic partnership. But the infrastructure and will exists.

The city doesn’t need an AI high school. It needs a thoughtful, distributed, teacher-led research effort that takes the history of educational technology seriously—and reimagines innovation not as the technology but as a problem-solving process that leverages the energy and creativity of communities.

That’s what my wife and I agreed on, somewhere around the second cup.

There’s a line early in the new New York City Public Schools (NYCPS) Guidance on Artificial Intelligence that stopped me cold: “We build relationships no algorithm can replicate. We see growth before it appears in data. We see promise where numbers fall short.”

That’s a sentence written by someone who has been in a classroom. And it matters, because this document, released in March 2026 to govern how AI is used across the largest public school system in the country, will shape the daily reality of nearly a million students and 75,000 teachers. Whether it shapes that reality well depends on whether it lives up to moments like that one.

I’ve spent the past many months exploring the global landscape of AI education frameworks: from UNESCO’s competency frameworks to the OECD’s AILit initiative, from China’s national mandate to Finland’s trust-based model, from Digital Promise’s equity-centered literacy framework to AI4K12’s Five Big Ideas. I read the NYCPS document with all of that context in mind.

Before I share my impressions, let me underscore that the AI in K-12 space is both crowded and at times a bit frenetic. Keeping pace with every new framework or model or white paper is exceptionally challenging. What I offer is the best of what I have been able to see, understand, and wonder. What it comes to NYCPS, here is what I have found.

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The governance is strong. Genuinely strong.

The Traffic Light framework (Red for prohibited uses, Yellow for professional judgment, Green for approved) is one of the clearest, most actionable risk frameworks I’ve seen at any level, anywhere. It names what AI will never be allowed to do before it names what AI can do: no automated decisions about student placement, discipline, or graduation; no AI-generated IEPs; no surveillance; no AI as substitute for counseling. Each prohibition is mapped to specific Chancellor’s Regulations and federal law, giving it legal teeth that many frameworks lack.

The document is also unusually honest. “We will not pretend to have answers we do not have,” it says. “We will not wait for certainty that may never come. And we will not let uncertainty become an excuse for inaction.” In a policy landscape full of documents that sometimes project more confidence than warranted, this epistemic humility is both refreshing and strategically wise. It positions the system as a learning organization, not an authority handing down tablets.

And the equity framing is structural, not decorative. The document begins with three specific students: the fourth grader whose reading lags behind her curiosity, the multilingual learner navigating two languages, the student with a disability whose classroom lacks the right tools. That’s not an abstraction. That’s a purposeful design constraint. The text consistently argues that the students who depend most on public schools are the ones most affected when its AI governance fails. And most empowered when it succeeds.

But governance without pedagogy falls short.

Here’s where I start to wonder. The NYCPS guidance is primarily a regulatory document. It tells educators what they’re allowed to do and what they’re not. What it doesn’t do, at least not yet, is tell them how to teach well in an AI-mediated environment, or help students become critical, thoughtful users of AI rather than supervised consumers of it.

Student use of AI sits in the Yellow category with strikingly thin guidance: “Students may use AI for research, exploration, and creative projects. Educator guidance, critical evaluation of outputs, and age-appropriate context are required.” For a system of nearly one million students who are already using AI extensively outside of school, not to mention the tens of thousands of teachers who are already using AI in their practice, that’s not enough. There’s no framework for what responsible student AI use looks like in practice, no scaffolding by developmental stage, and no guidance for how students should think critically about AI’s outputs, biases, and embedded assumptions.

There is also little or no grade-band specificity. A kindergarten teacher and a twelfth-grade AP teacher receive the same document. Compare Vermont’s framework, which specifies no chatbots in PreK–2, curriculum-embedded AI in 3–5, structured educational chatbots in 6–8, and broader fluency in 9–12. Or China’s tiered developmental approach. The NYCPS document acknowledges this gap and defers it to the June 2026 Playbook, along with the AI literacy curriculum, the bias review criteria, the academic integrity guidance, and the outcomes framework. That’s a lot of weight for one Playbook to carry in three months.

Most strikingly, the document makes no reference I observed to existing national or international AI education frameworks. Not UNESCO’s teacher competency framework. Not the OECD/EC AILit Framework that will shape the widely-regarded PISA 2029. Not Digital Promise’s AI Literacy Framework, with its insistence that understanding and evaluating AI must precede using it. Not AI4K12’s Five Big Ideas, which offer exactly the kind of grade-band progressions the document says it needs. Not aiEDU’s AI Readiness Framework, which has already synthesized all of these into a single architecture, complete with student competencies by grade band, educator competencies, and a school leader readiness rubric that addresses exactly the principal-as-instructional-leader role that determines whether policy becomes practice. NYCPS appears to be building from scratch when well-developed resources already exist.

What you see and what you don’t.

There’s a deeper problem, too. The entire framework assumes, like many other frameworks, that AI is something users choose to use. It’s a tool you evaluate, approve, and consciously bring into instruction. But increasingly, AI doesn’t arrive that way. It arrives sneakily inside platforms teachers and students are already using: adaptive features in textbook software, AI-generated recommendations in learning management systems, algorithmically synthesized answers in search engines, smart suggestions in Google Docs. None of these announce themselves as AI tools. They’re just the platform doing what it does. The governance process NYCPS described (called ERMA) is built for discrete procurement decisions: a school identifies a need, submits a request, a vendor gets reviewed. It isn’t built for the quiet creep of AI capabilities into already-approved products through a software update no one flags. And the document’s commitment to transparency and explainability, that families and educators should understand what AI tools do and why, depends on knowing that AI is operating in the first place. A parent can’t ask questions about an algorithm they don’t know exists. A teacher can’t exercise the professional judgment the document rightly demands if the AI is invisible to them.

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No framework globally that I have seen has adequately solved this problem, but NYCPS might be well-positioned to be the first to name it: AI is not just a tool you adopt; it’s a functionality you’re probably already working with. And it is completely changing what it means to work with digital, well, anything. Notably, aiEDU’s framework comes closest to addressing this pedagogically: its 9–12 student competencies include evaluating AI tool use within larger technology ecosystems, identifying how AI tools connect with and impact other technologies students already use, and acting as a decision-maker around AI tool use rather than just a consumer. That’s the kind of critical capacity the NYCPS document gestures toward but doesn’t commit to.

So, what is the city to do?

NYC’s tension is familiar to anyone who has had the honor of working in this system. (I had such an honor as a teacher from 2003 to 2009; at the city offices from 2009 to 2011; and as a consultant for many years after.) Here’s how the tension tends to go. The central office wants coherence; schools want autonomy; pedagogy has historically been a local decision. So how does one balance local and administrative control? Visible uses of AI and invisible uses?

Maybe the answer is in the room (and beyond).

One answer is already sitting in every school: the Danielson Group’s Framework for Teaching (FFT). Every NYC teacher knows it. It’s how their teaching is officially described, observed, and evaluated. An AI-informed interpretation of FFT could define what good teaching looks like when AI is part of the environment, not by adding new mandates, but by clarifying existing standards. What does Domain 1 planning look like when AI assists material development? What does a Domain 2 culture of learning look like when cognitive offloading is a risk? What does Domain 3 intellectual engagement look like when students have AI at their fingertips? These are pedagogical questions, not compliance questions. And they honor teacher judgment.

The other answer is to draw on the international community. This one might strike you as odd, so let me explain. The number of students and teachers in NYC schools is about the same size as Ireland (yes, the country). When NYC school officials are looking for insight and inspiration, it is fitting to turn both within (other states and US districts) and without (education ministries in other countries). Not only is NYC massive in terms of size and people served in its schools, but mayoral control gives the chancellor similar authority as an education minister might have abroad. That means, for instance, that UNESCO’s three progression levels (Acquire, Deepen, Create) could structure professional development in NYC. Officials should look at other countries, including those who might be described as economic competitors.

The Playbook drafters at NYC should look closely at what aiEDU has built, which I have to imagine they are. The AI Readiness Framework, now in its second version, informed by Burning Glass Institute labor market data and grounded in cognitive science research on how AI affects learning, offers something NYCPS needs urgently: an integrative architecture that connects student competencies, educator competencies, and school leader readiness into a coherent system. Its three student domains (Know Your Basics, Be a Critical Thinker, Lead with the Human Advantage) provide the developmental scaffolding the NYCPS document lacks, organized by K–5, 6–8, and 9–12 grade bands. Its educator domains run in parallel, positioning teachers not as compliance followers but as professionals who model the critical thinking and human judgment their students need. And its school leader rubric, with progression levels from Demonstrate Commitment through Invest and Implement to Deepen and Iterate, gives principals a roadmap for the instructional leadership that makes or breaks implementation in a system where 1,800 schools each operate with significant autonomy.

aiEDU’s framework isn’t perfect. Its “Human Advantage” framing leans toward workforce readiness, and it doesn’t fully engage the critical literacy questions about whose knowledge and power AI systems encode. But it has done the synthesis work that NYCPS hasn’t yet attempted, drawing on UNESCO, Digital Promise, AI4K12, and the OECD/EC framework to produce something genuinely usable. The June Playbook doesn’t need to start from zero. Much of what it needs already exists.

In sum: governance is necessary, but what kind of pedagogy will it serve?

That’s an inspiration question. And it’s the one the NYCPS’s document—smart, honest, and necessary as it is—hasn’t fully asked.

Yet.

Perhaps June’s Playbook will start there.

“You know,” she said, “it’s easier for me to find a smoke shop in my neighborhood than a bookshop.”

I understood what she meant. Anyone walking down the street could see it: ample opportunities to buy cigarettes, precious few to buy a book. But I found myself wondering how literally true her statement was, not just on her stretch of sidewalk but across the entire city. So I dove into the data.

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A Sidewalk Comment Becomes a Dataset

First, I went to the State website and downloaded the most recent dataset on tobacco sales licenses. Then I cobbled together my own inventory of booksellers: records from the Independent Bookstore Association, the larger chains I could identify scattered around the five boroughs, and every public library branch. It was not a perfect dataset; I will concede that freely. But it was a thorough one, assembled in response to a single afternoon’s offhanded comment. I merged the two lists and visualized them on a map.

The results were stunning.

Why the Map Matters

Access to literacy stands as one of the strongest indicators of a child’s preparedness for school. Researchers have documented what they call the “million word gap”: the staggering difference in language exposure between children who are read to regularly from birth and those who are not. A child whose evenings brim with stories, whose parents narrate the world aloud, whose household treats reading as ordinary rather than aspirational, arrives at kindergarten with a formidable advantage. The inverse is equally true and equally consequential.

Literacy, though, is not merely a skill drilled in classrooms. It flourishes in the informal spaces between parents and children: bedtime stories, conversations about a character’s strange decision, the idle flipping of pages on a Saturday morning. All of that matters immensely. And all of it requires, at minimum, the physical presence of books.

So if you want to understand why literacy scores in New York City remain stubbornly low, one question deserves more attention than it typically receives: what kind of access do families of different socioeconomic backgrounds actually have to books and reading experiences? Mapping smoke shops against bookshops renders that question visible, geographic, irrefutable.

Igniting Reading

What does it mean to make literacy genuinely available to families? When I worked at that nonprofit, we sent thousands of books into the homes of children between the ages of zero and five. Those books made some difference; I believe that. But even that effort was not enough. Libraries remain perennially underfunded. Too little emphasis falls on the quiet, unspectacular importance of simply having exposure to books and to reading experiences.

I am grateful for the comment that mom made to me that afternoon in the Bronx. A passing observation on a sidewalk became a dataset, then a map, then a picture of a city that has made it easier to light a cigarette than to open a book.

We can do better than that. We have to.

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I have been thinking about that ancient suspicion a great deal lately, because I have been writing with AI. After all: our word technology is etymologically rooted in tekne.

Creativity Has Often Been Outsourced

The discomfort arrived early. Speaking ideas into a document and then feeding that raw material to a language model to help shape and compose felt like a kind of evasion; inspiration on my end, execution outsourced. But the more I sat with that unease, the more I suspected it was pointing somewhere more complicated than a simple moral failing.

Consider the Renaissance studio, where a master would conceptualize a painting and leave vast portions of the execution to apprentices. The vision was the master’s; the brushwork, often, was not. The documentary Exit Through the Gift Shop modernizes this unease almost to the point of parody: Mr. Brainwash runs a factory of assistants producing work at scale, and the documentary pointedly refuses to tell you whether what emerges is art or elaborate hoax. The question of authorship, it turns out, has always been less stable than we assumed.

Musicians too. A guitarist’s wah-wah pedal inserts something mechanical into the moment of expression yet leaves something irreducibly human intact. Beat production in hip-hop goes further still, with machines doing substantial structural work. Nobody seriously argues the producer’s artistry has dissolved into the circuitry. What persists through all that mediation is worth naming carefully: the originating vision, the animating idea that existed before any tool touched it.

The Original Tech Pessimist

Which brings me back to the sophists, and to the man who found them most alarming.

Socrates was, among other things, a committed technological pessimist. The great disruptive technology of his age was writing itself, and he distrusted it profoundly. Commit thought to the page, he argued, and you produce something that looks like knowledge but cannot defend itself; it will say the same thing to everyone, cannot answer questions, cannot sense when it is speaking to the wrong audience. Writing, for Socrates, was tekne in its worst form: a simulation of wisdom that let people skip the hard interior work of actually becoming wise.

He had a point. He also delivered it exclusively through conversation, leaving nothing written down.

The Student Who Saved Him (With the Thing He Hated)

This is where the story gets delicious, and a little ironic as only the Greeks could do. We know everything Socrates thought because Plato disagreed with him. Plato took his teacher’s spoken arguments, the ones Socrates trusted precisely because they were alive, responsive, unmediated, and committed them to the permanence of text. The man who feared writing was preserved by it. Without Plato’s willingness to reach for the very technology his teacher condemned, Socrates would be a footnote, or nothing at all.

So Which Is It?

Here is the question our moment presses on us: is AI the new writing, a tool that looks dangerous but will ultimately carry human thought further than any previous technology? Or is it something closer to the sophist’s textbook—a device that generates the appearance of insight, flatters its users into believing they have said something, and quietly erodes the interior life that makes genuine thought possible?

I do not think the answer is obvious, and I am suspicious of anyone who delivers it with confidence in either direction. What I do think is that the distinction Socrates drew, however much he overcorrected, remains the right one to keep in focus: there is a difference between a tool that extends and sharpens thought, and a tool that substitutes for it. The wah-wah pedal does not feel the music for the guitarist. The printing press did not write Paradise Lost. Whether AI belongs in that lineage of honest amplification, or whether it is something the sophists would have recognized and immediately tried to monetize, depends grealy on what the person behind the prompt brings to the room.

The Cup at the End of the Argument

Socrates was not wrong about everything. His suspicion of easy knowledge, of borrowed eloquence, of wisdom retailed in convenient packages: all of it still cuts.

The tools we trust with our thinking have always carried consequences. The question is whether we are thinking clearly enough to see what they are.

The Athenians eventually decided Socrates was a problem. They handed him a cup of hemlock and called it democracy.

Someone asked me recently what potential I saw for AI in education. I have a lot of thoughts about this. Too many, probably. But here’s the honest version.

If you’ve studied the history of technology in American schools, you already know the script. A bright new tool arrives with breathless promises. Philanthropists write checks. Districts sign contracts. Consultants descend. Money changes hands. And then, after the fog of enthusiasm lifts, classrooms look remarkably similar to how they looked before. The overhead projector. The interactive whiteboard. The laptop cart wheeled in like a sacrament. Billions spent; precious little transformed.

AI will follow much of that same arc. It will make some people very rich. It will waste staggering amounts of public money. Vendors will peddle products built on buzzwords rather than pedagogy; school leaders, pressured to appear forward-thinking, will buy them. This is not cynicism. It is pattern recognition.

And yet. I do think AI’s stickiness will prove greater than what we’ve seen from previous technologies. Some things will take hold in ways that genuinely matter. The place where I see the most tantalizing potential is one that rarely makes headlines: large-scale assessment.

Assessment, for better or worse, has demonstrated over the last two decades that it dictates what happens in classrooms. Standards set aspirations; tests enforce them. The instruments we use to measure learning shape the learning itself. That reality frustrates me, but ignoring it helps no one. So the question becomes: can we make those instruments smarter, more humane, more reflective of what students actually know and can do?

I think AI opens a door here that has been sealed shut for a very long time. Consider oral assessment. There was an era when speaking constituted the primary mode of demonstrating knowledge. You stood before your teacher, your class, your examiner; you answered questions in real time; you presented, defended, revised your thinking aloud. Orality was foundational to how we understood a student’s mind.

That tradition faded in lockstep with the rise of large-scale testing technologies. A few years ago, my colleague Nadia Behizadeh and I wrote about this very entanglement: how the evolution of assessment instruments has been inseparable from the evolution of the technologies used to administer them. Multiple-choice items didn’t triumph because they were pedagogically superior. They triumphed because they were scannable. The Scantron machine, not learning theory, determined the dominant grammar of American assessment.

Now picture this. AI systems can process natural language with startling fluency. They can listen, transcribe, interpret. The very method I used to compose this post — speaking my thoughts aloud while a machine helped me shape them into prose — hints at a broader possibility. What if we could administer oral assessments at scale? Not as a novelty or enrichment activity, but as a legitimate, widespread mode of measuring what students understand?

I find that prospect genuinely thrilling. Speaking is a mode of communication every bit as rich and rigorous as writing; it simply requires different capacities. A student who struggles to organize thoughts on paper might articulate a devastating interpretation of a poem when given the chance to speak it. A child whose first language isn’t English might reveal conceptual understanding through dialogue that a timed written exam would never surface.

If AI can help us build large-scale assessments that are authentically multimodal — assessments that honor speaking alongside writing, that welcome visual and performative expression — then we stand to benefit more learners and more teachers than the current regime of fill-in-the-bubble tests ever could. The promise isn’t that AI will grade students for us. The promise is that AI might finally allow us to listen to them.

Will this happen cleanly or quickly? Of course not. The history I invoked at the start of this piece guarantees mess, false starts, corporate opportunism. But beneath all that noise, there is a signal worth attending to. For the first time in over a century, the technology exists to let students speak at scale and be heard. That possibility alone keeps me paying attention.

The science of reading has commanded increasing attention over the last several years. (Thanks is owed, in part, to this viral podcast series.) At its best, this movement has renewed a valuable focus on building students’ literacy skills and taken seriously the question of how children learn to decode and comprehend text. That focus is welcome.

But the picture is more complicated than the movement’s loudest advocates tend to acknowledge. Some highly respected literacy researchers argue that the science of reading overstates its own science — that the neuroscience of how the brain processes text is a far cry from any neuroscience that might guide the actual teaching of reading in a classroom full of living, fidgeting, complicated children. Knowing how synapses fire when a person decodes a word does not automatically translate into knowing what a teacher should do on a Tuesday morning in October with twenty-eight third graders, each carrying a different history with language. The leap from brain scan to lesson plan is longer than the branding suggests.

I raise that not to dismiss the movement but to insist on precision. And precision matters here because of a second, larger problem: when we allow literacy to be defined as reading, we mistake the tree for the forest.

Literacy is Bigger Than Reading

Reading is one part of what it means to be literate as a person in the world. It is not the whole of it. And yet, from a de facto perspective, that conflation is precisely what has happened in American education. I say de facto because, as I’ve argued elsewhere, tests dictate what gets taught and what gets valued in our schools. The tests overwhelmingly define literacy as reading. Reading is by far the thing that English Language Arts assessments attempt to measure. Writing, where it appears at all, receives no serious treatment — certainly not in New York State, where I’ve spent most of my career. Speaking and listening? Assessors don’t even attempt it. So literacy, in practice, collapses into reading. The other dimensions of a literate life quietly vanish from the ledger.

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At first glance, that collapse might not seem terribly alarming. Reading matters enormously. But reading alone doesn’t actually make you particularly literate. It makes you someone who can read. There is a difference. If you cannot read for aesthetic pleasure; if you cannot take what you’ve read and discuss it with others, wrestle with it, challenge it; if you cannot then compose something that critiques or extends or tangles with the ideas you’ve encountered — if you cannot put something more nuanced and more yours out into the world — then one has to ask whether you meet any robust definition of literate. I am not sure you do.

Arguably, this is exactly what we’ve been doing in the United States for the last twenty-some years. In our earnest effort to take literacy seriously, we have suffocated it. We reduced it to reading alone and called the job done.

A T-Shirt that Makes the Point

Here’s an illustration that haunts me a little. If you spend enough time in schools around New York City, New York State, really anywhere in the country, you will inevitably encounter a quotation attributed to Frederick Douglass. I’ve seen it printed on posters, painted on hallway walls; I once saw it rendered as a full mural at a school entrance. The quote says, “Once you read, you will be free forever.” (I have yet to find the quote in Douglass’s writings, however.)

There is something deeply inspiring about that line, and the life of Frederick Douglass — what he accomplished for American education, for Black Americans, for the very idea of human dignity — cannot be overstated. His story is incandescent.

Reading is Not Enough

But there is also something incomplete about the way that quote gets applied to schools. Because it isn’t actually reading that empowered Frederick Douglass. It was his capacity to read and to write. We only know he might have uttered those words because he possessed the ability to set his thoughts down on paper with extraordinary force and precision. If Douglass could only read, we wouldn’t know how brilliant he was. We wouldn’t know him at all. His letters, his speeches, his autobiographies — these are the artifacts of a fully literate person, someone who absorbed the ideas of others and then forged something devastatingly original from them. Reading was his foundation. Writing was his freedom.

So when schools plaster that quote on their walls while simultaneously gutting writing instruction and ignoring oral expression, the irony cuts deep. They celebrate Douglass the reader while forgetting that Douglass the writer is the reason we have his work to celebrate.

Reading is necessary. It is also insufficient. It has to function as a building block, a foundation toward full literacy. And full literacy has to involve not just absorbing the ideas of others but critiquing them, discussing them, debating them — and then ultimately creating something for others that is remarkably yours and is itself worth being read. That final step, the leap from consumer to creator, is where literacy becomes power. Without it, we produce a nation of people who can decode sentences but struggle to compose one that matters.

We can do better. We owe it to Douglass, among others, to try.

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Here’s a detail that startled me when I first encountered it: the word rubric enters English in the 1400s from a French word meaning red. It referred to the instructions printed in red ink inside liturgical books, telling clerics exactly how to conduct church services. Rubrics, in their earliest incarnation, served as interfaces between individual human beings and authoritative institutions. They constrained variation. They preserved orthodoxy. They channeled the messy, unpredictable impulses of real people into something the institution could recognize and control.

Sound familiar?

The Quiet Machinery

I spent years studying how software operates in education, and I kept circling back to rubrics. Not because they’re inherently villainous; they aren’t. A rubric can be a generous tool, a way of saying to a student or colleague, here is what we value, and here is where you stand in relation to it. That version of the rubric reflects fairness back to individuals. It lives in conversation. It breathes.

But something different happens when rubrics feed data into information systems. When that occurs, the rubric stops functioning as a social instrument and starts functioning as a computational one. It becomes, in effect, a piece of software disguised as a piece of paper.

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Consider what software demands. Databases require unambiguous information filed into clearly defined categories. Algorithms need quantifiable inputs. Control statements operate in binaries: if/then, either/or. The scholar Lev Manovich calls this database logic, and he argues it seeps into culture as software becomes omnipresent. When rubrics serve as the gateway to information systems, they impose that same logic on teaching. They transcode the irreducible complexity of a classroom into neat, storable, retrievable parcels of data.

The False Choice Between “Helping” and “Deepening”

Let me show you what I mean with a concrete example. New York State’s edTPA, a teacher certification assessment born from $700 million in Race to the Top funding, includes a rubric that asks evaluators to judge whether a teaching candidate “helps students understand strategies to construct meaning from complex text” (Level 3) or “deepens student understanding of strategies to construct meaning from complex text” (Level 4).

Read those two descriptions again. Slowly.

The grid draws a hard line between them. The visual architecture of the rubric insists these are distinct, separable categories. But written language permits ambiguity, metaphor, overlap; the visual logic of the grid does not. Could a teacher help some students while deepening the understanding of others in the same lesson? Of course. That would be excellent differentiation. The rubric, however, cannot tolerate such nuance. It forces a false binary because the database waiting downstream needs a single, unambiguous number.

The evaluator must choose. Three or four. The richness of what actually happened in that classroom gets flattened into a digit that can travel through fiber-optic cables to servers belonging to Pearson Education, where it will eventually be cross-referenced with student test scores and used to evaluate the teacher preparation program that produced the candidate in the first place. A thousand-dollar fee accompanies the privilege.

There’s a Category for THAT

I watched a video once, published by a school district as a model of good evaluation practice, in which a principal and assistant principal debated whether a teacher deserved “Basic” or “Proficient.” The rubric language distinguishes between these categories using phrases about “moderate cognitive challenge” versus “significant cognitive challenge,” about lesson structures that are “recognizable” versus “clear.” Yet the administrators’ actual conversation hinged on something the rubric never mentions: whether the teacher arrived at the observation with his lesson plan already polished or needed in-the-moment coaching to sharpen a few minor details. The assistant principal had originally scored the teacher as Proficient. The principal talked him down to Basic.

That single word, Basic, then traveled from the conversation into the city’s online evaluation system, where the assistant principal selected it from a dropdown menu next to “Domain 1(e).” He clicked a button. Software sent the quantified representation of this teacher’s professional identity to a city database integrated with state systems, which packaged the data for national reporting. Algorithms applied preordained categories and canned responses. The system might eventually recommend professional development products the teacher could purchase from a for-profit vendor.

This is the pipeline. Rubric to interface to database to algorithm to marketplace. The teacher receives what a former Secretary of Education once called “feedback about their practice.” But feedback implies a conversation. What actually happens looks more like genuflection.

Software Won’t Replace Teachers; It Will Seduce Them

I want to be precise about what worries me, because I think the popular fear gets it wrong. The danger is not that machines will replace teachers. The danger is subtler, more corrosive, and already underway: as we interact with software in certain prescribed ways, we slowly defer to computational logic and the pedagogies it encourages. Teachers won’t be replaced by software. They will be seduced into thinking and teaching like it.

Rubrics accelerate that seduction whenever they serve as the transcoding mechanism between lived experience and database logic. Each time we reduce a classroom observation to a number, file it into a predetermined category, and click “submit,” we rehearse the syntax of software. We practice its grammar. We internalize its epistemology, which holds that everything worth knowing can be quantified, stored, retrieved, and algorithmically acted upon.

What Rubrics Could Be Instead

None of this means rubrics must serve these purposes. A teacher educator could use the edTPA rubric to sit down with a candidate, talk through what happened in the lesson, identify strengths and struggles, and chart a path forward. No data feed. No profit-seeking company lurking at the end of the pipeline. No public reporting. If the genuine goal were to improve individual practice, this approach would be more fitting and more effective.

The same holds for teacher evaluation. Principals who treat observation rubrics as conversation starters rather than data-entry forms honor the complexity of what teaching actually is. The trouble arises when the system demands that the conversation’s conclusion be compressed into a dropdown selection and transmitted to entities whose interests extend well beyond the classroom.

We live in an era that Manovich describes as “everyware,” where software powers our phones, our markets, our transportation, our wars. Some of these entanglements with software enrich us; some constrain us; many do both simultaneously. The question for educators is not whether to engage with software but how to engage with it without surrendering the humanistic core of what we do.

Rubrics sit at the fulcrum. They can reflect fairness back to individuals, or they can transcode human experience for the benefit of databases. They can serve as earthly interfaces, grounded in the social world of classrooms and conversations, or they can serve as holy interfaces, channeling the irreducible messiness of teaching upward into a sanctified system that treats data as divine.

The choice matters. It matters every time we pick up a rubric, every time we click a button, every time we watch the complexity of a human interaction vanish into a server rack. That schoolish sacrament of pedagogy belongs to us. We should be reluctant to let it slip through genuflecting fingers.

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It might surprise you to know, however, that the word education appears nowhere in the federal Constitution. Neither do schools, learning, or teaching. The Tenth Amendment sends anything not explicitly addressed (or forbidden) to the states. Education landed there not because the Founders wrote it out of the picture deliberately, but because they never wrote about it at all.

When I first stumbled on that omission, I felt genuinely stunned. No national articulation of education’s purpose in a democracy? That means fifty states could define it fifty different ways. And they do.

Fifty Systems, Not One

We speak casually about “the American education system” as though it were a single organism. It isn’t. It is fifty organisms, each with its own constitutional DNA, each under no obligation to coordinate with the others. Most industrialized nations maintain one centralized framework guiding what education means and how it operates. The United States has over four dozen. The phrase “American education” is, strictly speaking, a misnomer; what we have are American educations, plural, scattered across state lines like dialects of a language nobody bothered to unify.

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Consider what this means in practice. One state’s constitution might enshrine education as a fundamental right, guaranteed to every child. Another might frame it as a legislative duty, subject to the whims of budget cycles and political fashion. A third might describe education in florid, aspirational language that sounds magnificent and commits to almost nothing. These are not minor semantic differences. Constitutional language shapes funding formulas, court decisions, curricular mandates, and the very question of who deserves access to a classroom. When fifty states answer that question independently, the result is not pluralism so much as fragmentation.

This is the quiet chaos hiding in plain sight. We argue endlessly about standardized testing, teacher pay, school choice, and curriculum wars, yet rarely pause to notice that the combatants are fighting on fifty different battlefields with fifty different rulebooks. There is no shared constitutional floor beneath public education in this country. The Founders, whatever their reasons, left that floor unbuilt.

Making the Invisible Visible

A question gnawed at me: what if I could help people compare those definitions side by side? What if I gathered the education clauses from all fifty state constitutions, linked each one to its original text, and plotted them on an interactive map that anyone could explore with a click? So that is exactly what I did, in a project I called “States of Education.”

Visit the map and you see a red pin in each state. Click one and a bubble opens, revealing the passage from that state’s constitution that first describes the purpose of or right to education, followed by a link to the primary source. The whole thing took about ten lines of code to build. That brevity still astonishes me; the barrier between a burning question and a public, interactive tool can be remarkably thin.

What struck me most during the building, though, was the reading. I sat with constitutional language from every state in the union, and the contrasts leapt off the spreadsheet. Some states treat education as sacred. Others treat it as a line item. The cumulative effect of reading them together is vertiginous: you begin to grasp just how profoundly uncoordinated the American approach to public schooling really is.

Why This Matters Now

The consequences of this constitutional silence grow louder by the decade. As families move across state lines, as national crises demand coherent educational responses, as technology reshapes what and how children learn, the absence of a shared federal commitment to education becomes less a quirk of history and more a structural vulnerability. We expect schools to sustain democracy, cultivate citizens, and equip young people for a bewilderingly complex world. Yet we have built no constitutional consensus about what that work even means.

My observations about the Constitution and education’s conspicuous absence from it might have remained confined to a column, a conversation with friends, a classroom lecture. Instead, I transformed a question and a spreadsheet into an interactive visualization that makes state constitutions infinitely more accessible to educators and communities. From such interaction, new questions might emerge. Perhaps even grassroots refinement of state legislation.

It seems fitting that a tool designed to illuminate what the Constitution left unwritten should itself be a small act of democratic participation: an invitation to read, to compare, to ask whether fifty separate answers to the most important question in public life is truly the best we can do.

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This is a reworked version of a column called “The Computer’s Constitution” originally appearing in English Journal.

That’s the percentage of time children actually spend in school each year. Do the simple math. Most states require roughly 180 school days. Multiply that by about six hours a day. Add it up and ask: what fraction of all the hours in a given year does that represent? The answer is just over twelve percent. Students spend approximately 88% of their lives each year somewhere other than a classroom.

Sit with that for a moment. When you internalize this number, two things happen.

Two Big Lessons

First, you gain a staggering appreciation for what teachers accomplish. A single English teacher might see a student for, what, forty-five minutes a day? Across an entire year, that teacher works with dozens of young people in a sliver of time so thin it almost defies measurement. Whatever growth occurs in that sliver — and real growth does occur — qualifies as something close to miraculous. Teachers deserve far more credit than the reform conversation tends to give them.

Second, and perhaps more consequentially, you begin to understand why researchers talk so much about out-of-school factors. These are all the conditions that shape a child’s preparedness to learn before they ever walk through the building’s front door. A teacher whose students have their basic needs met might think about these factors less. But teachers whose students experience poverty, that’s a different story. Is the child safe at home? Can they eat breakfast? Do they feel secure walking to school? Can their family afford the bus fare? Are they getting adequate health care? Do they need glasses? Each of these factors exerts a disproportionate influence on what happens once a student sits down in a classroom. The 88% overloads the 12%, relentlessly.

The Education Reform Paradox

Here lies one of the great ironies of education reform over the last few decades: you cannot reform education by focusing on schools. Standards, tests, curricula — all of these matter, of course. But stack them together and you are still only addressing twelve percent of a student’s life. The sooner we understand this at the local, state, and national level, the sooner we can move past the circular arguments and political posturing that have consumed so much energy and public money while leaving the deeper conditions of children’s lives largely untouched.

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This is what makes some of the ongoing research from the Learning Policy Institute so compelling. Their work keeps surfacing one model as among the most promising approaches to genuine school improvement: Community Schools. The concept is deceptively simple, though not easy. A school purposefully partners with community-based organizations to address what is happening during the 88% of time students aren’t in classrooms. Health clinics operate on school grounds. Social workers connect families to housing assistance. After-school programs provide safe, enriching spaces. Food pantries become part of the school’s ecosystem rather than something families navigate alone.

Community Schools can be expensive, and they can be tricky to get right. The model depends heavily on the individuals representing each partner organization who come to the table; when those relationships fracture, the whole architecture wobbles. But difficulty is not an argument against seriousness. It should be studied further, funded more generously, and treated as a legitimate pillar of education strategy rather than a feel-good footnote.

Teach the Teachers

There is another insight buried in the 12% that I think we overlook. When we design teacher education and ongoing professional learning, we tend to frame the work narrowly: what do teachers need to know in order to support students in the classroom? That framing isn’t wrong, but it is incomplete. We should also be helping teachers understand how vital the 88% is and what role they can play in bridging it. If your students mostly come from the surrounding neighborhood, do you know where the food pantries are? When you call home, are you only asking about grades, or are you learning something about the texture of that child’s life beyond your walls? Is there information about a student’s social and emotional state that might illuminate why they stare out the window every afternoon? These questions aren’t peripheral to pedagogy. Research tells us, with mounting clarity, that the answers to them bear directly on a student’s capacity to learn anything at all.

We have spent decades trying to optimize what happens inside classrooms. That work is not wasted. But if we keep pretending that the 12% can compensate for the 88%, we will keep arriving at the same disappointing results and wondering why. The math isn’t complicated. We just have to be willing to do it.

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My story follows had a slightly different twist.

What’s Teaching, Anyway?

I had no designs on teaching. None. I was a philosophy and English major at Fordham, steeped in jurisprudence and narrative, aimed squarely at law school. Teaching did not occupy a single neuron of my professional imagination. The classroom, as a vocation, simply did not exist on my radar. I had spent the better part of two decades sitting in classrooms, absorbing the work of countless teachers, and yet the act of teaching itself remained invisible to me. It was like oxygen: everywhere, essential, unnoticed.

Then came the jazz.

I had the improbable good fortune of landing an internship at Jazz at Lincoln Center. My duties were humble and glorious in equal measure. I attended rehearsals of the Jazz Orchestra, set up chairs and music stands, and did whatever the band needed to prepare for the next performance. I was a twenty-something kid in a room full of extraordinary musicians, setting up coffee and bagels, watching artistry unfold at close range. For someone who had always loved music (I went to music school originally, on a a singing scholarship), this was a kind of paradise.

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Central to these rehearsals was the music director: Wynton Marsalis. A true virtuoso who moves between the classical and jazz worlds with a fluency that borders on the supernatural. Simply being in the same room while he conducted the orchestra filled me with a joy I struggle to articulate even now. The music poured through the rehearsal space; it climbed the walls; it settled into your bones.

A Virtuoso Teacher

One afternoon, I sat in a corner of their rehearsal studio in Hell’s Kitchen—a fairly dingy space, fluorescent lights humming overhead, nothing glamorous about it—studying for the law school admissions test. The band was preparing for an upcoming concert, and things were not going well. The orchestra wasn’t performing the way Wynton wanted. Frustration crept into the room like fog; you could feel it in the musicians’ shoulders, in the tightening of Wynton’s jaw.

And then something shifted. Rather than pushing harder, Wynton slowed everything down. He began to explain his vision for the piece, not in abstract terms but with precision and patience, working first with individual musicians, then with small groups, then pulling the full ensemble back together. He listened. He demonstrated. He coaxed and cajoled. He made the invisible architecture of the music visible.

I put down my test prep book.

For the first time in my life, it occurred to me that teaching is its own thing. Its own purposeful, intentional craft. Not a byproduct of expertise; not something that simply happens when a knowledgeable person stands in front of others. Teaching is a practice: deliberate, artful, loving. Wynton was not merely sharing what he knew about music. He was reading his audience, diagnosing what stood between them and the sound he heard in his mind, and constructing a bridge from where they were to where they needed to be. He was, in every sense of the word, teaching.

This revelation stunned me with its obviousness. How had I spent nearly two decades in schools without recognizing that what my teachers did constituted a distinct and sophisticated art? The answer, I think, is that truly skilled teaching can render itself transparent. When it works, it feels effortless, almost natural, as though understanding simply arrived on its own. The craft disappears into its own success.

That afternoon in Hell’s Kitchen, I watched the craft become visible. And once I saw it, I could not unsee it.

Questions Like Notes

I closed the test prep book for good and began exploring teacher education programs, specifically in teaching English, my second major. The rest, as they say, unfolded from there. Twenty-some years later, I find myself still inside the questions that moment opened. They resound like the notes from a trumpet: What constitutes learning? What constitutes teaching? What exactly is the relationship between learning and teaching? Isn’t all learning and teaching to some extent rooted in inspiration?

My roots in education trace back to a scuffed-up rehearsal studio, the sound of a jazz orchestra finding its footing, and one virtuoso who showed me that teaching is never incidental. It is always an act of intention, imagination, and care.

Thank you, Wynton.

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Bonus: Watch Wynton Go

It has been twenty years since a group of scholars proposed a paradigm shift for how we understand literacy practices in the 21st century. Coining the term “multiliteracies,” the New London Group offered a framework for embracing multiple literacy practices, including the use of emerging digital technologies, as essential for students and teachers to “design social futures”

(Cazden et al.).

In 2016, at the annual conference for the American Educational Research Association, two members of the famed literacy researcher collective, the New London Group, reflected on their influential paper. Bill Cope noted that with such rapid change in the technological sector, it is imperative that a new generation of scholars continues renewing the Group’s powerful yet aging theory. Much research uses multiliteracies as a theoretical starting point: digital literacies, new literacies, new media, and transliteracies are just a few of the better- known lines of inquiry. And yet, since 1996, there has remained a particular limitation to both the New London Group’s original paradigm and the ways research in multiliteracies has evolved.

Put simply, multiliteracies scholars have limited their work to researching and theorizing the relationship between new technologies and literacies nearly exclusively to what occurs on and above the screen. That is, the rich work we as a field have conducted has focused on what it means to use digital technologies to compose and critique texts in the world, treating digital technologies as instruments akin to 21st-century pens, pencils, and typewriters. What we have not yet acknowledged is that digital technologies are themselves linguistic constructs. That is, below the screen, there exists a world of computational and human languages that mediate and shape the very kinds of social futures we can design above the screen with digital technologies. In what follows, I make a brief theoretical case for what goes on below the screen, which I call sub-screenic literacies, as a necessary space for literacy researchers to examine in the digital age.

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Sub-screenic literacies refers to the computational and human languages that are used to create software—including AI. It refers to things like computer code and APIs (application program interfaces), which are long strings of data pushed to and pulled from systems. It also refers to human-facing texts that are essential to software such as developers’ documentation and other communications between programmers. Sub-screenic literacies are not just “code.” In fact, the word code oversimplifies the complex subscreenic assemblages that software theorists expose: as code/space, as the language of new media, as inherently unstable, and even computer code as itself a form of speech.

When multimodal researchers examine digital phenomena, it is not uncommon for the inquiry to focus on ways that digital technologies are used by people to communicate and create things in the world. What they tend to leave unexplored, however, is the way that sub- screenic literacies make possible what happens above the screen, what I refer to as super-screenic literacies. An illustration will be helpful. In a study I did several years ago on the design of an online English course used in New York City schools, I used methods of multimodal analysis similar to what scholars in the field typically use. For example, I analyzed how much of what users saw on the screen was related to curriculum (i.e., literary content), how much was administrative (i.e., tracking one’s progress), and how much was more functional (i.e., navigating to different sections of the course). I also examined the buttons users clicked to interact with the course. My analysis demonstrated how a particular online course provider positioned students as “managers” of their learning, limiting their learning experience to consuming content.

Had I, at the time, used more sub-screenic methods of analysis, I might have found myself asking different questions. For example, when one encounters a multiple- choice question in an online course—which is sadly typical—there are three main programming languages developers use to create what one sees on the screen. Hypertext markup language (HTML) controls the main text and structural relationship between components on the webpage, looking something like this:

<!DOCTYPE html>

<html>

<head>

<title>Dynamic Quiz Project</title>

<link type=’text/css’ rel=’stylesheet’ href=’stylesheet.css’/> <link rel=”stylesheet” type=”text/css” href=”http:// fonts.googleapis.com/css?family=Open Sans”/>

</head>

<body>

<div id=’container’>

<div id=’title’>

<h1>Moby Dick Quiz</h1>

</div>

<br/>

<div id=’quiz’></div> <div class=’button’ id=’next’><a href=’#’>Next</a></div> <div class=’button’ id=’prev’><a href=’#’>Prev</a></div>

<div class=’button’ id=’start’> <a href=’#’>Start Over</a></div>

<!-- <button class=’’ id=’next’>Next</a></ button>

<button class=’’ id=’prev’>Prev</a></ button>

<button class=’’ id=’start’> Start Over

</a></button> -->

</div>

The cascading style sheet (CSS) determines how the raw elements of HTML will appear on a webpage—things like font types, colors, design of the buttons, and overall layout —and might read partially like this:

body { font-family: Open Sans;

}

h1 { text-align: center;

}

#title { text-decoration: underline;

}

#quiz { text-indent: 10px; display:none;

}

.button { border:4px solid; border-radius:5px; width: 40px; padding-left:5px; padding-right: 5px; position: relative; float:right;

background-color: #DCDCDC; color: black; margin: 0 2px 0 2px;

}

And then there’s JavaScript. JavaScript powers the elements and activity of the quiz itself (i.e., the text within the quiz, the logic that accepts some answers and not others, and what happens with the data generated from a student taking the quiz). It might resemble this:

(function() {

var questions = [{ question: “What is the first word in the book?”, choices: [Ishmael, You, Call, Me, Once], correctAnswer: 2

}, {

question: “What is the month Ishmael uses to describe his soul?”, choices: [May, September, March, July, November], correctAnswer: 4

}, {

question: “Better sleep with a sober than a drunken Christian?”, choices: [cannibal, whale, pirate, dog, shadow], correctAnswer: 0

},

The answers in the JavaScript code above have to be predetermined by the developer. In most cases, the developer would rely on a content-area expert to provide the questions and “answers.” Does the fact that JavaScript in this case requires predetermined and circumscribed answers to questions (if it is to automate assessment) force curriculum experts to perpetuate multiple-choice-style questions? How does the cost of writing code like the example above affect the kinds of software-powered products companies produce? Are there other uses of programming languages that might be more expensive but could support students in asking their own inquiries and exploring them more authentically? These are just some questions that emerge when sub-screenic literacies are briefly theorized and explored.

When we engage with and research super-screenic literacies, which is overwhelmingly where multiliteracies researchers have focused their energy for the last two decades, we must acknowledge that such literacy practices are limited by the sub-screenic literacies that make software possible. This is increasingly important in an age of AI. More accurately, our super-screenic literacy practices are limited by those who produce and control sub-screenic literacies. We have spent 20 years mapping the tip of a linguistic iceberg. It’s time to suit up, dive in, and explore the unfamiliar depths of software space, so full of darkness and breadth and life.

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In some ways, this might seem counterintuitive: Why would teachers benefit much from a program that, by definition, focuses on out-of-school time?

Numbers Don’t Lie

As a nod to our new Chancellor’s background as a math teacher, let’s use some simple calculations to answer that question. Roughly speaking, students are required to be in school 180 days a year for approximately 6 hours a day, which amounts to 1,080 hours a year. Sounds like a lot, right? It’s not. There are 8,760 hours in the calendar year. That means students are only in school for a little over 12% of their lives each year.

Students spend 88% of their time out of school.

It’s the paradox of education reform: you can’t reform education by focusing alone on schools; you can’t improve student outcomes by focusing alone on teachers. Students’ out-of-school lives in their communities and with their families have a disproportionate impact on their readiness for the classroom. Safe streets, loving homes, healthy meals, and creative outlets all combine to ensure children arrive in school open and ready to learn.

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My Always-Late-Student

When out-of-school factors go unaddressed, teachers often shoulder the responsibility for making up the difference and ensuring that their students succeed anyway. It is too often an impossible task. I experienced this first hand as a high school English teacher in Manhattan. One of my students arrived for my first period class twenty minutes late every day. When I asked him what was going on, he shared that his parents needed him to work late at the family restaurant to make ends meet. I was able to get him to arrive fifteen minutes late and then ten minutes late and then ultimately he came on time. But he was still exhausted; his parents didn’t answer my calls.

In the near future, teachers in our city might well be able to do their jobs with greater effectiveness and efficiency than ever before as the benefits of universal child care manifest. More students will be ready physically and emotionally for learning. More parents will have the bandwidth to communicate with teachers and participate in school governance and events.

But that future state will not happen automatically. Something else needs to be done--and soon.

Call it The Mamdani Plan

If the new mayor and chancellor are going to ensure real impact from this historic opportunity, they will need to do something no other administration has been able to pull off: build a true bridge between parents and school, from birth to graduation. There are some signs that officials know such ambitious bridge-building is needed. Chancellor Samuels has signaled that the role of parent coordinators--unsung heroes of our system who serve as a school’s single point of contact for families--will be getting refreshed resources. Mayor Mamdani spoke recently about fortifying the official mechanisms in the city for parents to get involved and have their voices heard. All excellent materials for building the needed bridge.

However, if the bridge is going to really be effective, the mayor and chancellor must quickly assert its length, ensuring it has a span of eighteen years. That is right: eighteen years. Many mayoral initiatives in New York City education tend to be short-term programs, not comprehensive plans. They are designed with more like an eighteen-month span focused on accomplishing something that officials can point to as a victory in time to fundraise and campaign for re-relection. It is a sad side-effect of mayoral control that merits refinement.

What has been missing over the past two administrations is a comprehensive eighteen-year plan for children that spans from the moment a child is born to the moment a child is supposed to walk across the graduation stage. Such a plan would ask: What do families, teachers, and researchers say children need both in school and out of school from ages zero to eighteen? What role will every city agency, New York City Public Schools being one, play in making sure children and families get what they need at each age level? How do we coordinate agencies to provide precisely what children and families need when they need it? Universal childcare is one crucial part of that plan. Universal Pre-K and other early childhood programs is another part. NYC Reads yet another. Now is the time to put it all together. Heck, call it the Mamdani Plan.

Why Now

We as a city have not yet been able to traverse the stubborn achievement gap successfully because we haven’t had a strategic bridge of sufficient design and span. As someone who has worked in New York City education for over twenty years, I can tell you that our teachers have shouldered much of the bridge-less burden. But if we use the questions above to drive aggressive plan development process right now, Governor Hochul and Mayor Mamdani might just be able to say they did not just improve test scores but that they improved the education system itself. With that accomplishment will likely come re-election, yes. But more importantly, they will secure something of arguably even greater value: legacy.

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