Here’s the recipe: a paper book, a slow voice, and a room where nobody can hide. Thirty years ago, that was just called class. Now it reads as radical, and the professors running it are experimenters testing the variable of friction.

As educators, we spent twenty years optimizing friction out of school. Shorter readings, smoother interfaces, answers just a search away. Remove the obstacles, the thinking went, and learning flows. I won’t pin the whole decline on the smoothing; phones, a pandemic, and two decades of test policy left their marks, too. But the record is in, and it isn’t kind. Students can decode every word and yet lose the thread of a long argument. They arrive at college, Rose Horowitch reported in The Atlantic in 2024, “bewildered” by the expectation of finishing whole books. (The word belongs to Columbia’s Nicholas Dames.) Does this bother you as much as it bothers me?

So, a counterculture is forming around one insight: some friction is not an obstacle to learning; some friction helps to produce the learning. A paper book is harder to skim, which makes it easier to actually read. Reading aloud is slower, so the difficult sentences and passages can’t be skipped. Working through a text together makes confusion public, and public confusion is survivable. For PublicSource, Jamese Platt profiled Pittsburgh faculty who read passages aloud, line by line, and have students write their own questions before class begins. No one can hide in the silence; that’s a skillful design.

Cognitive science got there thirty years ago. In 1994, the UCLA psychologist Robert Bjork named the pattern desirable difficulties, and the lab results since have leaned lopsided in one direction. Studying that feels easy, rereading the highlighted page, produces memory that fades by Friday. In contrast, studying that feels like a struggle, generating an answer from scratch, wrestling with a passage before being told what it means, produces understanding that stays. Ease is a feeling, not a learning outcome. The students who feel most fluent quickly are often learning the least.

The methods are more precise than paper, good; screens, bad. At the University of Pittsburgh, the sociologist Hillary Lazar pairs stronger and weaker readers in what she calls a one-room schoolhouse, so that comprehension happens out loud, between people, rather than failing silently in private. Call it what it is: the exact effort that reading requires, measured out, and stirred back in by hand.

Here’s my claim, stated plainly. The effort was never the bug; it was the mechanism. Take the difficulty out of reading, and we don’t get easier learning. We get the look of reading without the cognition that made it worth anything.

One objection deserves a straight answer because, no doubt, friction can be a barrier. For instance, the dyslexic reader needs the audiobook, and the working parent needs the flexible format. The boundary that matters runs between the friction in access and the friction in the text. Clear the first completely, but guard the second, because the productive struggle is what helps us learn.

And one caveat the movement’s admirers tend to skip: nobody has published outcomes data on these classroom practices. There are decades of laboratory evidence for the principle, a handful of professors applying it on purpose, and data I’m watching for, but that is not here yet.

Still, I find this counterculture hopeful—and I don't get to say that often on this beat, where most of what crosses my desk is decline. The story of rediscovering deep reading is a bright spot precisely because it isn't a platform. It won't scale or break like one, either. Good. Let the tech titans go target something other than a core function of civil society.

Deep reading was never going to come back by removing effort; it comes back by designing the right kind of effort, and perhaps more importantly, by valuing that effort. The professors who went old-school looked most closely at where we are and decided that the way forward runs through the difficulty, not around it. Even though the ingredients look like 1990, the recipe is the most forward-looking thing on campus: a paper book, a slow voice, and a classroom where nobody can skip the thinking.

A question for the comments: what’s one piece of useful friction, in reading or anything else, that you’d put back if you could?

Read deeply,

Dr. Genevive

I like Angela Collier’s videos about physics and I was delighted to discover this 18-minute one…

…because it’s a great continuation to the thread about the complexity of Microsoft Office I shared recently.

Collier talks about why physicists prefer LaTeX to Word. LaTeX is sort of a nerdy HTML that predates HTML. It looks like this…

…and given how nerdy HTML already is, you might imagine this is a power-user tool that’s chiefly about power and control. But Collier makes the argument that there are some things that LaTeX makes much easier:

• there is absolutely no need (or peer pressure) to spend time styling the document by choosing fonts, colors, etc.,
• there is no “live preview,” and making a PDF is a separate step similar to compilation in coding – which means it doesn’t constantly occupy your mind,
• GUIs can slow you down because the keyboard is faster than the mouse,
• LaTeX doesn’t give you a lot of control over positioning, which is better than giving you only a semblance of control over positioning (this is the TikTok meme Collier alluded to briefly).

This is really interesting because it goes right to the core of the uncomfortable truth: naïve design decisions meant to make things easier might achieve the opposite. I shared the ForkLift example where the team didn’t understand what made the previous version great, and more recently the animation that could slow people down.

(Of course, there is also the issue of typographical craft of LaTeX documents set in Computer Modern, but let’s save this for another time.)

Also, the video starts with Collier apologizing for potentially making the audience feel dumb in a prior video. I don’t think it’s a joke, and I found it thoughtful and refreshing.

#attention #complexity #enshittification #flow #youtube

Fiction written by artificial intelligence is easy to detect because it struggles with complex story structure and tends to moralize in clunky ways, according to a preprint study from researchers at University of Maryland, College Park and Google DeepMind. They found that AI fiction has tells that go beyond stereotypical overuse of em-dashes and other obvious AI tropes and have more to do with the formulaic nature of the text itself.

“AI stories over-explain themes and favor tidy, single-track plots while human stories frame protagonists’ choices as more morally ambiguous and have increased temporal complexity,” the study, which looked at more than 50,000 AI-generated short stories, found. “Claude produces notably flat event escalation, GPT over-indexes on dream sequences, and Gemini defaults to external character description. We find that AI-generated stories cluster in a shared region of narrative space, while human-authored stories exhibit greater diversity. More broadly, these results suggest that differences in underlying narrative construction, not just writing style, can be used to separate human-written original works from AI-generated fiction.”

Basically, AI-generated fiction sucks and at the moment is easy to detect. The typical method of detection involves looking for stylistic markers such as an abundance of em-dashes, the overuse of the word “delve,” or an obsession with goblins, but this project tried something different. “The idea for this project came because we are hoping to eventually move past plain text detection, into some sort of space where we can separate human ideas from AI-generated ideas,” Jenna Russell, a University of Maryland researcher and one of the study’s authors, told 404 Media. Russell is also an intern at the AI-detection company Pangram.

Russell and her team decided to attempt to detect what she called “narrative features” in AI- generated fiction. The detector is called StoryScope and it builds on NarraBench, a 2025 benchmark that suggested a taxonomy of narrative features in fiction. StoryScope looked at how fiction handled plot development, character descriptions, setting, and temporal structure to determine if something was written by a human or an AI.

“It was my first attempt at getting 'under the surface' and focusing more on ideas,” Russell said. “We wanted to see how close to typical AI-detection we could get by only relying on the narrative features, to understand if this sort of structural difference really even exists. This method also adds some interpretability to detection, which is an open question in the field. Using narrative features, we can point to certain tangible features (such as the number of subplots included in a story). I think this is why it's struck a chord recently, people can really say ‘ah these are some of the underlying traits of how AI writes fiction.’”

To test StoryScope, the researchers selected 10,272 human-written stories then reverse engineered them into writing prompts using Gemini 2.5. Then it took those thousands of prompts and fed them into Gemini 3 Flash, DeepSeek V3.2, Claude Sonnet 4.6, Kimi K2.5, and GPT 5.4. All of the data — including the prompts and the resulting AI stories — are available on Hugging Face.

To source the stories, the researchers used the Books3 dataset — a database of 183,000 books collected from pirated ebooks. The dataset is the subject of several lawsuits and has been used to train an unknown number of LLMs. The StoryScope study included more than 10,000 of some of the most famous short stories ever written, many of them pulled from popular anthologies. There’s Joyce Carol Oates, Stephen King, Louis L'Amour, Charlotte Perkins, and Harlan Ellison. All have been rendered down to their base elements by AI and then regurgitated into a different LLM to see if it can replicate them.

Russell told me the dataset was controversial. “Hence why we do not release it to the public,” she said.

The study itself contained a disclosure. “We acknowledge the copyright issues related to the Books3 dataset and do not endorse its use for model training or commercial text generation,” it said. “The use of the dataset in our paper is restricted to academic purposes only and is meant to understand the narrative differences in human-written and AI-generated text to help inform discussions on AI-detection, authorship, and copyright policy.”

The various AIs, of course, can’t possibly replicate the prose of O. Henry. So what, according to StoryScope, are the narrative quirks of LLM-written simulacra of English’s grand works of fiction? 

AI tools tend to over explain themes, for one. 

“Narrators explicitly explain the story’s theme 77% of the time, versus 52% for humans: a grieving character’s arc will typically end with the narrator stating the lesson learned. AI dialogue serves philosophical debate more often (59% vs. 34%), and references to other works tend to be vague allusions (72% vs. 50%) rather than specific, named references. The pattern is one of over-determination: AI spells out meaning rather than trusting the reader to infer,” the study said.

AI also more often avoids subplots and fails to play with time jumps and flashbacks. The systems overwrite passages about the body and senses. “Where a human author might write that a character ‘felt afraid,’ AI renders fear as a tightening chest, cold sweat, and dimming lamplight,” the study said. Humans also spin more complicated narratives involving more characters and locations than AI can handle. Humans also reference other works of fiction, specific people and places in a way that AI struggles with.

A disclosure caught my eye at the bottom of the StoryScope study. “Large language models and coding agents (Claude Code and Codex) are used to aid with and polish writing and generate some tables and plots,” it said.

“I believe it's important to disclose AI use (and ideally think it should be more in-depth than I wrote in the paper),” Russell told me. “Most researchers are using AI, a lot of it seemingly 'slop' [...] but a lot of it is high-effort, good research. Also, technically you are supposed to disclose AI use for conference submissions, but most people don't. I want to help change that norm!”

She also explained a bit more about how AI agents helped shape the project. “I use AI agents to help implement the code (using the claude code / codex interfaces). I also use them as an editor during the writing process! They have access to the project codebase and the paper latex, so the agents can implement graphics for me much more quickly than I could,” she said. “They write comments and add to the paper draft, but I keep it all in different colors so I can manually review and accept/reject/edit any suggestions from AI. I am a big believer that AI can help or hurt writing, but usually helps when not used to create more internet 'slop'.”

I kept thinking about Harlan Ellison and Robert Silverberg’s story “Ship-Shape Pay-Off” being turned into an AI prompt and then spit back out by an LLM. Ellison died in 2018 and was notoriously protective of his work to the point of violence. He successfully sued James Cameron for plagiarism over The Terminator. I have a hard time imagining he’d be happy to see his story pumped into a machine, no matter the results.

“A lot of people, like teachers or readers, don't really care if AI was used in the writing process, but do care if the human is the one behind the heart of it,” Russell said. “A teacher wants to know if their student understood the lesson, and a reader wants to know that the creativity behind a touching story was truly the work of the human author.”

The reality is to make augmented reality glasses, you need to put a camera next to your eyes that is continuously recording everything you see and processing that to put information over it.

There is not another way around it. And there's certainly not a chip that can fit in the stem of a glasses that is both powerful enough and power miserly enough to do that in real time.

You have to send that data to a cloud. You gotta do it. [...] Or you can build something the size of a Vision Pro with a battery pack that lives somewhere else. Those are the current choices in this world.

And it means if you want to build the product that everyone thinks is the next thing, you are going to have to invade people's privacy.

And maybe you shouldn't. Like, there's an incredible argument for, nope, you shouldn't do that. Nope, the trade-offs required to make this product are so high at a societal level that we should stop it.

— Nilay Patel, The Vergecast

Tags: ai-ethics, augmented-reality, nilay-patel, privacy, ai

The medium is the message.

-Marshall McLuhan

I built a math fact practice website a few years ago. It’s still up, you can check it out if you like. I’m not a professional developer, it’s not anything too fancy. The basic idea is to adapt to the learner. The website tries to figure out which facts the student needs help with and gives them focused practice on those facts. When I’m teaching a class of 25 students, it’s hard to meet every student where they are. One advantage of technology is the ability to adapt a learning activity to each student with far less effort from the teacher.

As I tested and used the website with students I found disadvantages as well. One is “videogamification.” A typical student spends plenty of time playing video games on screens. Many spend a significant chunk of their school day playing video games as well, as those of us who are familiar with Slope and Subway Surfers can attest. In a video game, the goal is to beat the level as quickly as possible, not to learn along the way. Students bring those habits with them when screens come out in math class. In the case of my math fact website, some students would deliberately get early questions wrong in order to give themselves an easier path through the task. I tried lots of stuff to mitigate this, but I kept running into the same fundamental problem: students were trying to get through the task, not trying to learn. I ended up building a paper-and-pencil system for math fact practice to replace the website.

We could have an interesting debate here about the details. I could try to redesign the website. Maybe the benefits of personalization are worth the drawbacks of screens. I don’t want to get stuck in this specific example. Every time I’ve used screens with students, I have found videogamification to be an obstacle. It’s part of the medium. Those habits students have built elsewhere carry over to classroom technology, and that changes my decision-making when it comes to screens in school.

Technology Is Not Neutral

I’ve played a small role in the classroom technology backlash, and the backlash to the backlash is here. Among many others, Zach Groshell argues that it’s not the screen, it’s the instructional design underneath. Groshell compares the delivery of instruction, whether a screen or a human teacher, to a grocery truck. It’s not the truck that matters, it’s the food inside. “Again and again, people seem to care more about the carrier they can see than the design they cannot.” Groshell’s argument is that we shouldn’t care whether students learn from a screen or a human teacher; we should strive to deliver excellent instructional design to as many students as possible. The vehicle, to Groshell, is neutral.

I’ve named one counterexample: videogamification, the tendency for students to treat anything on a screen like a video game. And to be clear, students sometimes treat paper-and-pencil learning like that as well. My observation, based on my experience as a teacher, is that videogamification is an order of magnitude worse on screens than on paper.

That’s just one way the medium shapes the message. Another example with a broad base of research is in reading comprehension. A wide range of studies have found that comprehension is stronger when reading happens on paper compared with a screen. This is a fundamental property of the medium: humans skim and struggle to attend deeply when reading on screens.

Putting screens in front of students has advantages and disadvantages. One advantage is that the screen can look back at the student. Here’s Carl Hendrick sharing what that advantage affords online instruction:

Schooling fulfills the function of accountability. In the future, this will mean cameras on, and an AI monitoring student behavior, their latency, what websites they are looking at, their ability to focus and then producing a report for a human tutor. If you can get kids to concentrate on the apps, on the sequencing, phenomenal learning is going to happen.

If you spend time watching students learn on screens, this is totally unsurprising. It’s the logical endpoint of making screens the primary vehicle for instruction. Students are prone to rushing, to skimming, to finding the easy way out. If AI is the solution to our educational problems, of course we should have AI monitor students’ eye movements.

Carl isn’t wrong. Accountability is critical in education. One of my most important roles as a teacher is being a caring human in the room with students. That presence, our relationship, and my ability to hold students accountable for learning, are huge elements of what motivates students to learn. If (this is a big if) AI can monitor student “latency” and everything else, I have no doubt it will increase student learning in a tech-first environment. But the medium is the message. Accountability from artificial intelligence is different than accountability from a human. What message does that send to students? What kind of life does it prepare them for?

Let’s Talk About Tradeoffs

I would love to have a serious, honest conversation about the tradeoffs here. Teaching is hard. I sure would like to see a way to scale the impact of the best teachers. Teacher quality is uneven; I know well that too many students spend their school day on busywork, whether it’s delivered from a screen or a packet. Is AI the solution? Maybe.

But the starting point of that conversation is to acknowledge that the medium is not neutral. The medium is the message. The tools we use shape us. I’ve felt it. Part of the reason I moved away from classroom technology was my realization that when there’s a screen in front of every student, I became a more passive observer in the classroom, deferring to the machine to manage instruction. That decision isn’t neutral.

There are serious, smart people doing work that acknowledges those tradeoffs. Dan Meyer is building an AI tool designed to help teachers launch discussions building on student thinking, recognizing that digital classrooms risk isolating students and that teachers benefit from tools to counteract that isolation. Daisy Christodoulou is building an AI tool to radically reduce the time teachers spend assessing student writing, while keeping a human in the loop. It even works on handwritten essays, so teachers can keep screens out of the classroom while still harnessing the power of today’s technology. These are great examples of real tools that wrestle with the affordances and liabilities of today’s technology.

The internet is full of big claims about what AI can do, yet the vast majority of those claims are hypothetical: what could be, where we might end up, what is possible. I am heading back to the classroom in exactly one month. I will be making decisions based on the real resources available to me, not hypotheticals on social media. Despite some big claims, the apps that Groshell and Hendrick are developing are not yet available to boring teachers in traditional schools like mine. We can’t yet evaluate the tradeoffs of using them with students.

I’m moving down from middle school math to elementary in August. While I’m still a strong skeptic of the role of technology in the classroom, I don’t think I will be tech-free. I’ll use a bit of student-facing technology here and there, for specific purposes or as I’m required to by my school. I’ll keep experimenting with new technology, and I’m optimistic that I’ll develop more and more tools that will make planning easier for me using large language models and more. I will think hard about the advantages and disadvantages at each step of the way. That’s all any teacher can do: be open to new ideas, be clear-eyed about tradeoffs, and do the best we can with the resources we have.

I have to call a spade a spade. With the digital tools I have access to right now, paper-and-pencil classroom teaching is almost always the better option. And the vision of education that some people are selling, where technology unlocks a brave new world of efficient learning, is smuggling something sinister under the hood.

The students booing artificial intelligence at commencements across the country are not just worried about jobs. They have learned an urgent lesson from the not-so-distant past.

They know that the familiar promise of empowerment and creativity will continue to give way to the pathologies of the online surveillance economy: viral slop, commercial manipulation and addictive apps — this time on automated steroids. 

The utopian promise of the tech industry is on life support. The hope that it would empower workers and revitalize democracy soured sometime between the massive data breach of the Cambridge Analytica scandal and the rapid uptake of the term “surveillance capitalism” to describe the online economy. 

If Silicon Valley once received the enthusiastic reception reserved for “good guy capitalists,” those days are over, and deservedly so.

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The backlash is not limited to AI. The luster and hype surrounding the entire tech industry in the 1990s and 2000s, back when Gen Xers and millennials flocked to Silicon Valley, have fizzled, replaced by mass layoffs and a litany of social harms. 

It’s not only that Gen Z has lost faith in Big Tech. In the face of galloping economic inequality and democratic backsliding, many now view tech titans as greed-fueled latter-day barons of capitalism.

Gen Z has learned that what determines the future of technological innovations is not their inherent capabilities but the choices of the private organizations that deploy them. Students worry that AI will enhance the data-driven manipulation of consumers and flood the media environment with synthetic clickbait. 

These young people are already seeing what technology is doing to their lives and education and don’t like the results. At my own institution, students have formed a Luddite Club to resist the siren song of social media, and they’re not alone. 

In our short-attention-span era, it isn’t easy to hark back to the heady days of the early web, when we were assured everyone would benefit from access to the accumulated knowledge of the world and become active participants in well-informed self-governance. The futurist George Gilder predicted in the 1990s, for example, that the personal computer would become “a powerful force for democracy, individuality, community and high culture.” 

Today’s generation was not around for any of that, and now they are up against the reality the tech industry actually delivered — not the fantasy it sold. They are confronting the fact that what matters is not just the technology, but the social relations in which it is embedded.

Instead of cultural uplift and the creation of an informed citizenry, young people see billionaires profiting from pumping the most sensational and polarizing viral content into our news feeds. 

Instead of prosperity, they see the real wages of working Americans in decline and a country in which the richest one percent control more wealth than ever before. They see Amazon founder Jeff Bezos sending his fiancée and a pop star into the stratosphere while Amazon workers pee in bottles and collect food stamps. 

Instead of a vibrant information-enhanced multicultural democracy, they see a country sliding into authoritarianism and corruption at an unprecedented scale while platforms hire teams of psychologists to help addict young people to online brain rot. 

Related: What it’s like to enter the job market in the middle of an AI revolution

In the face of these developments, the tech oligopolists remain in something of a time warp. They look in the mirror and fail to see the caricature of extreme, unaccountable wealth they have become; they strain instead to recapture the image of themselves as hip young founders in hoodies parading through plush Silicon Valley campuses while promoting “don’t-be-evil” happy capitalism. 

The ubiquitous venture capitalist Marc Andreessen encapsulates this midlife crisis. A one-time founder of the web browser Netscape, he recently bemoaned the demise of the “deal” whereby tech moguls were revered by the media, awarded honorary degrees “from all the universities” and invited to “all the great parties.”

If tech billionaires are too cocooned in their fabulous wealth to absorb the lessons of history, this year’s crop of college students is not. They see a bigger picture: a world with powerful AI tools in the hands of a few companies devoted to using our own data to control and manipulate us. 

They see a present in which companies with unprecedented surveillance power are prostrating themselves before an increasingly authoritarian administration bent on targeting its perceived political foes. 

During his commencement address at the University of Arizona, former Google CEO Eric Schmidt responded to the AI skepticism of graduating seniors by urging them to play a role in shaping the future of AI. He was seemingly attempting to revive the promise of an earlier digital age. Schmidt, 71, is old enough to remember when those claims held currency, while today’s students are not. 

They have quickly learned what earlier generations have been slow to admit: When billionaires pledge to empower the world, they usually only mean themselves. 

Mark Andrejevic is a professor of media studies at Pomona College. 

Contact the opinion editor at opinion@hechingerreport.org.

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