It's the Reading, Stupid

We obsess over what AI does to student writing. The bigger threat is to reading.

One of the perks of a teacher’s summer is the pile of books we now have time to tackle. I estimate that I’ve spent upwards of six hours a day reading over the past twenty-five years. That’s not unusual - I suspect many of my readers engaged in knowledge work exceed that count. A rough calculation puts that at more than 54,000 hours of reading. Using a modestly high reading speed, that easily translates to more than a billion words over that time.

This means I can tell at a glance whether a Google AI summary is likely to be on target, whether I need to read deeper on the topic, quickly locate a trusted article or source to skim, zero in on the section I need, or whether I should regroup entirely and start a new search. I do this effortlessly, without much thought, and feel confident about the process. Most students can’t. Students will not or cannot read AI output with the understanding or the skepticism it demands, because most do not have anywhere close to a billion words under their belt to parse its language. The overlooked issue is that if we actually want students to use AI effectively, either in class or at home, the skill we need to worry most about is not writing. It is reading.

How to Read a Book

Last summer I argued that the teaching method in the Great Books program I finished was the one AI can’t touch. One flaw in that argument is if students start off-loading their reading to AI. But even if they do, many students do not have the ability to read AI summaries carefully, let alone ones based on classics like The Republic or The Odyssey.

A book I had heard of but never read before the St. John’s program is Mortimer Adler and Charles Van Doren’s classic How to Read a Book (Van Doren of Quiz Show fame). In it, the authors lay out a method for tackling more advanced texts. The whole book may seem dated, but it is worth reading, especially the opening chapters. My major takeaway is that we rarely teach more advanced reading skills to improve comprehension once we have finished teaching basic decoding in the early grades. More teachers would benefit from applying some version of the strategies they recommend.

A piece by Carl Hendrick sharpened my own thinking on the critical importance of reading.¹ A prompt typed into ChatGPT, he points out, contains almost none of the knowledge in the answer it produces. The model’s response comes from everything it absorbed during training. Human readers operate in much the same way, supplying meaning from a lifetime of accumulated vocabulary, background knowledge, and life experience. The difference, of course, is that while AI may have access to billions and billions of words on virtually any topic imaginable, what it doesn’t have is emotions, experiences, or human connections to the world that provide uniquely human interpretation when reading a text. An AI cannot be moved to tears at the conclusion of a novel even if it is able to identify the emotionally charged language embedded in a paragraph.

When students do not have the foundation built through thousands of hours reading on their own, they do not have the vocabulary or muscle memory necessary to impart deeper meaning, especially as texts get more complex. Unsurprisingly, reading is one of those activities which improves exponentially - the more you read, the better you read, the more you understand, and the better you read and understand, the more you want to read.

Having read more than a billion words is a by-product of my disposition and my profession - it provided the training corpus that lets me read as efficiently as I do, and students simply haven’t put in the time. That is one reason why AI’s fluent and confident output often goes unchecked.

As a history teacher, I am often guilty of the very thing Adler warns about, even though I have tried to make reading instruction more a part of my practice and feel even more compelled to do so in the age of AI. I assume kids read better than they do, and I am programmed to spend my attention on content. That assumption needs re-evaluating, especially as we head into a world where delivering content instantly, on demand, synthesized, and drained of its original meaning becomes commonplace. We still need students to handle literary fiction, dense secondary sources, and primary documents.

Different Kinds of Reading

Not all reading is the same, which is a crucial thesis of How to Read a Book. Reading a thriller for its plot is a different act from reading a novel for its ambiguities, which is different from reading a work of history or philosophy for its argument and its evidence. Many people read mostly for pleasure, passively, like watching a film. Few people want to struggle with more difficult texts on their own. Most students, even many of our most proficient readers, rarely tackle tougher texts independently. Online social media rewards short paragraphs, skimmable listicles, and now, because of changes to Google’s search, instant answers that arrive before the question is even fully formed.² Maryanne Wolf calls the capacity we are losing cognitive patience, the willingness to stay with a difficult passage long enough for its meaning to surface.³ Deep reading has always been the hardest level to sustain and the easiest to lose - even adults frequently report difficulty staying engaged in deep reading in today’s social media saturated environment that rewards and cements ever shorter attention spans. Any child in school today is going to exist in a world where instant answers will be the expectation, not the exception.

Evidence from the Classroom

One of the best courses I ever took, for my Master’s at Columbia’s Teachers College in the late 1990s, was titled Building Bridges and Breaking Down Walls. It was co-taught by two public high school teachers, one in English and one in History, in Manhattan. We met for three hours on Saturdays over twelve weeks. Unlike the education courses I took over the summer, where we read Foucault or Derrida or some other theorist at a fifty-thousand-foot level, I was teaching full time while enrolled in this class, so the fact that it spoke directly to the realities of the classroom allowed me to implement what I learned the following week.

One point that resonated was how English teachers tended to emphasize the language and intent of the author, while history teachers were more concerned that kids understood the content. But the overall focus of the class was the critical importance of literacy in general.

Read it Again

The English teacher related how, early in his career, his reflexive response when a student expressed difficulty with a text was to tell them to read it again. This happened several times, in the same class, with the same student. Each time, he simply said read it again. Finally it dawned on him that the “read it” part of the instruction was where things had gone sideways. The student could not read it in a manner that allowed for comprehension. He had been treating a reading problem as a motivation problem. It finally occurred to him that he needed to explicitly teach reading strategies.

That story has stayed with me because it matches my experience - most high school teachers assume, though rarely examine, that students can already read well. Most of us were trained in our subject, not in the pedagogy of reading. Much has been written about students’ poor reading abilities, some of it designed to go viral,⁴ but almost always in a helpless, throw-up-our-hands register or scathing dismissal of “kids today”. We are much better at observing the problem than proposing solutions.

I began folding the reading strategies we discussed into my middle school history classes. I built a handout of increasingly difficult passages, and we annotated with marginalia - text to text, text to world, and text to self reflections - along with questions, words or phrases that puzzled us, and whatever we could untangle behind each short section. The point of the lesson was that there are many levels of reading, and you should not feel ashamed or inadequate when you hit an impasse. Friction and struggle were the point.

I believed that because it described my own reading life. I have read everything in front of me since I was ten or so, including many books well over my head. All of that time invested built whatever persistence I have as a reader. You do not need to understand everything to benefit from a book.⁵ Struggling with a text you cannot fully grasp stretches the muscle and introduces you to ideas, vocabulary, and situations you have to work through yourself.

With students, we need to help them with more advanced texts. Luke Morin has written about watching his students flounder with a single demanding sentence from Ray Bradbury, not for lack of effort, but because they did not know enough of the words to make a coherent interpretation.⁶ Past a certain point, struggle stops being productive. Students shut down. Assigning useful books is largely a matter of calibration - and sharing your own struggles is a wonderful way to model for students both the joy and the challenges of working your way through a difficult text. One of my most enjoyable teaching experiences in my entire career was reading Plato, Aristotle, and Homer with my Great Books students this past year. I tried to emulate my St. John’s tutors, who offered conditional interpretations and invited students to share their views. Reading a challenging book together can be a magical experience provided everyone is equally invested.⁷

Reading in the Age of AI

AI has turned reading on its head. The overwhelming focus in school, online, and in faculty meetings is on the impact of AI on writing. That is understandable, since writing is what we grade and what we require as proof of learning.

Reading, though, is the unexamined half of the literacy question, and it is where I think we should be more focused, especially as more schools bring AI into the classroom for students to interact with directly.

Marc Watkins identified this early, back in 2024, in a post bluntly titled “No One is Talking About AI’s Impact on Reading.” He warned that our fixation on writing was letting the reading question slip by unnoticed. Two years later, with AI far more embedded in how students find and process nearly everything, the focus on reading continues to be eclipsed by coverage of AI’s impact on student writing.

The irony buried in all the attention to writing is that reading and writing have never been separable. Ask any writer and you will find a voracious reader. A student who does not read will struggle to write well, no matter how many outlines and graphic organizers we hand them. Schools need to spend as much time thinking about teaching reading as we do about writing, and many of the ways AI is being marketed to help students read are just as controversial as its writing assistance.

Should We Individualize Texts for Ability?

A few years ago I used Brisk⁸ to help a student learn important material, and the decision perfectly captures the skill-versus-content dilemma identified in my Building Bridges course. I had a tenth grader who could not summon the energy to read in class. During silent reading and annotation her head slumped onto the desk, and she told me, repeatedly, just like the student in my teacher’s story, that she did not understand it. It did not help that the text was a hard secondary source on why the new United States had to abandon the Articles of Confederation. It was filled with abstract terminology - federalism, states’ rights, separation of powers. I needed her to understand the content for the next assignment.

Between classes, I used Brisk to rewrite the material several grade levels down, so she could work through it with our learning specialist. It helped. She understood the basics and was able to participate in the next activity.

Was that the right use of AI? She got the content she would otherwise have missed. The personally tailored version of the reading did its job and was not something I could have replicated easily. On the other hand, the simpler text removed the friction and the struggle. Did that make her a better reader?⁹

I solved the content problem by skipping the reading problem. But if we multiply that single decision across every assignment in every humanities class, what happens? When every student can dissolve any hard text on demand, where do they have an opportunity to build those crucial skills?

My very first Substack post described an AI exercise I did with students in which I built a custom GPT trained on a chapter from our American history textbook. The key instruction was that students had to read the chapter first, then use the GPT to go deeper and ask questions. Part of the activity was also to hear how they felt about using AI this way.

Looking back, I thought it was an innovative use case, but it’s telling that I have not done it again. I am not opposed to using AI in select circumstances to support reading comprehension and probe a text more deeply, but I was not convinced students would actually read the original first if they knew AI was waiting to help with the analysis. As I said in the post, I would never want students using AI in place of reading the original, but, just as with AI-assisted writing, the risk of reverting to cognitive offloading was too high. The other takeaway was equally clear: the strongest students got the best results. The ones who already read and wrote well quickly figured out how to improve their prompts and, critically, actually read the output with care. That’s something every educator needs to remember whenever AI is brought into the classroom.

As students get older they can find ways to extract AI’s benefits - and there are many - but I am more and more convinced that the bottleneck to effective student AI use is mostly a problem of reading. The sycophancy and confidence of AI output - worsened by the fact that students rarely know sophisticated prompting techniques or have access to the most capable frontier models - means many are not getting the most effective AI experience. And models carry worldviews that almost no one is aware of - a recent piece in the Economist investigated the many ways frontier AI companies shape the values that show up in open-ended responses, according to their own goals and preferences.¹⁰

If teachers do use AI in class, they need to think hard about how to structure the interaction to reduce these tendencies. Whether that means asking the AI to respond at an age-appropriate reading level (you can specify the lexile band) or, better, capping each response at 250 words or fewer, the average student user is otherwise likely to be overwhelmed. These workarounds exist, and I am sure many teachers have found them, but from my vantage point, and from what I see in the media coverage of AI in classrooms, almost none of this is discussed.

We Have to Be More Deliberate About Reading Instruction

I say this as someone who considers himself a strong reader, which is what makes my worst school experience instructive. I was terrible at science, in retrospect, largely because I could never get through the textbook. The vocabulary stopped me cold and I never built the discipline to look up each term. When I did, it only led to more technical language, and with no context I understood, the definitions themselves were impenetrable. This is exactly what Hendrick describes. I could “read” as well or better than my peers but had none of the domain knowledge, so I could not puzzle through the passages. It is the same wall plenty of confident readers hit the first time they try to read a dense Supreme Court opinion, and the one our students hit daily in classes where we assume they can read but the content is completely foreign.

Legal reading was the mirror image of my science debacle, though not because it came any easier. Motivated no doubt by early exposure in a family filled with lawyers, I found that my favorite part of practicing law was research, though learning to read a legal opinion still took real time.I often forget that this did not come naturally to me either. It was built from poring over thousands of documents over years - annotating, analyzing, questioning, and writing about the issues. Legal training in many ways can be summed up as learning how to read like a lawyer.

The Pitfall of the AI Summary

Relying on AI summaries without ever examining the source is a serious problem. The difficulty of research today is no longer finding sources - an AI Deep Research report can surface more relevant material in minutes than most of us could in a week before the advent of AI. The difficulty is reading that report, with its hundreds of citations (many from Wikipedia or Reddit unless you write a rigorous prompt restricting it to academic journals and other vetted sources), and doing the real work of tracking down the best ones and reading them carefully. How many students - how many adults - actually do that? Driving home the importance of careful reading is going to be among the most critical things we teach in the coming years.

Reading is so essential to almost everything we do in school that it’s sometimes easy to forget just how important it is to be a fluent reader. It’s the cross-cutting skill for every subject. In the Age of AI, it may be the most overlooked, and most valuable, habit we can model and emphasize for our students. If I could wave a wand and instantly bestow one proficiency on every student I teach, it would be the ability to read deeply. So many doors open once that muscle is built. Reading carefully and critically may be the single most important weapon we can give students when it comes to using AI.

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To be clear, I am hardly a reading specialist. I have taken a few graduate classes, read some books on the subject, done some professional development, and, most importantly, worked with kids for over thirty years. I am more and more convinced that reading is what students most need, and that, for most secondary school teachers, it is something we are least prepared to teach.

There are many experts on Substack, but the two previously mentioned - Luke Morin and Terry Underwood - have influenced my thinking more than most, and both know far more about literacy and teaching reading than I do.

Terry Underwood has forgotten more about literacy instruction and research than I will ever know; go back through the archives of Learning to Read, Reading to Learn if reading pedagogy is a passion of yours. As I was finishing this piece he published a characteristically sharp case for how AI, used well, can help reading instruction.

Luke Morin’s Middle School Literacy Project is one of the most useful and humbling repositories of reading strategies I have found. And he writes beautifully.

If reading about reading is your jam, their newsletters are a goldmine for learning more about reading instruction.

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Connect With Me

Beyond this newsletter, I work directly with schools, educators, and other institutions about pedagogical questions raised by AI. Take a look at my website and reach out - I’d love to hear what you’re working on.

1. Carl Hendrick, Reading Comprehension Is Not a Skill (March 2026). Hendrick argues that comprehension depends on vocabulary and background knowledge rather than on transferable strategies. That thesis is contested in the research - I use the term skill throughout this piece. My goal is to get kids reading more intentionally and deliberately through whatever means we can. Whether it’s a skill or not in a technical sense is beyond my area of expertise.

2. Anne-Laure Le Cunff, writing in the New York Times (July 2026), reports that more than sixty percent of Google searches in the United States now end without a click, and draws on curiosity research to argue that collapsing the gap between question and answer suppresses the incidental learning that longer, messier searching produces. I argued a year ago that AI search would make reading more important, not less, when Google rolled out AI Mode; it is now ingrained in virtually every aspect of search.

3. Maryanne Wolf, Reader, Come Home: The Reading Brain in a Digital World (New York: Harper, 2018).

4. The starkest example I’m aware of is a widely shared essay by Hilarius Bookbinder, The average college student today (Scriptorium Philosophia, March 2025), in which a veteran professor calls most of his students “functionally illiterate” and unable to finish a serious adult novel. It drew more than fifteen thousand likes, which tells you how it resonated with people, even if he might be overstating the case. What it does not offer, and what almost none of these dunks on students offer, is what to do about it.

5. John Warner, You Don’t Need to Understand… (May 2026), argues persuasively that reading without full comprehension can be valuable in its own right, and even pleasurable.

6. Luke Morin, Wading Into the Deep End: What Reading Actually Requires When the Text Gets Hard (January 2026). Morin’s students could not predict from the Bradbury sentence because they did not know the words - a clear classroom illustration of Hendrick’s point, and of what too much difficulty looks like in practice.

7. Marcus Luther has been running his weekly reading of Bleak House for months, demonstrating how you can even do deep reading online with a motivated group.

8. Brisk was the first AI tool I found in 2023 that instantly transformed text into different reading levels. At the time it seemed cutting edge; today, modifying text by grade level barely merits a mention.

9. To be clear, I am not opposed to using AI to help students read certain kinds of things at their level. But I have mixed feelings about some of the other ways we may be using AI to summarize, distill, and condense texts that would be better read in their entirety. This is also complicated by the fact that tools like NotebookLM can be exceptionally helpful in synthesizing documents and text that are organized together. Like almost everything with AI, there are tradeoffs. But what’s acceptable for adults is not always beneficial for students.

10. See AI models’ values are very different from most people’s, The Economist (June 2026), which examines how the major AI labs influence the values that surface in their models’ output.