Journalist Natasha Singer reported this story from McPherson, Kansas. It appeared in The New York Times, March 29, 2026

Inge Esping, the principal of McPherson Middle School, has spent years battling digital devices for children’s attention.

Four years ago, her school in McPherson, Kan., banned student cellphones during the school day. But digital distractions continued. Many children watched YouTube videos or played video games on their school-issued Chromebook laptops. Some used school Gmail accounts to bully fellow students.

In December, the middle school asked all 480 students to return the Chromebooks they had freely used in class and at home. Now the school keeps the laptops, which run on Google’s Chrome operating system, in carts parked in classrooms. Children take notes mostly by hand, and laptops are used sparingly, for specific activities assigned by teachers.

“We just felt we couldn’t have Chromebooks be that huge distraction,” said Ms. Esping, 43, Kansas’ 2025 middle school principal of the year. “This technology can be a tool. It is not the answer to education.”

McPherson Middle School no longer gives students their own Chromebooks to use in school and take home. The laptops are now kept in classroom carts and used only for specific activities assigned by teachers.

McPherson Middle School, about an hour’s drive from Wichita, is at the forefront of a new tech backlash spreading in education: Chromebook remorse.

For years, giants like Apple, Google and Microsoft have fiercely competed to capture the classroom and train schoolchildren on their tech products in the hopes of hooking students as lifelong customers. For more than a decade, tech companies have urged schools to buy one laptop per child, arguing that the devices would democratize education and bolster learning. Now Google and Microsoft, along with newcomers like OpenAI, are vying to spread their artificial intelligence chatbots in schools.

But after tens of billions of dollars of school spending on Chromebooks, iPads and learning apps, studies have found that digital tools have generally not improved students’ academic results or graduation rates. Some researchers and organizations like UNESCO even warn that overreliance on technology can distract students and impede learnin

Schools in North Carolina, Virginia, Maryland and Michigan that once bought devices for each student are now re-evaluating heavy classroom technology use. And Chromebooks, the laptops most popular with U.S. schools, have emerged as a focal point. School leaders, educators and parents described the laptop curbs as an effort to refocus schooling on skills like student collaboration and conversation.

“We’re not going back to stone tablets,” said Shiloh Vincent, the superintendent of McPherson Public Schools. “This is intentional tech use.”

The classroom device pullback is the latest sign of a growing global reckoning over how tech giants and their products have upended childhood, adolescence and education.

In a landmark verdict last week, a jury found the social media company Meta and the Google-owned YouTube liable for hooking and harming a minor. More than 30 states have limited or banned student cellphone use at school. Last year, Australia began requiring social media companies to disable the accounts of children under 16, a move that other countries are considering.

Now children’s groups and educators concerned about screen time are turning their attention to school-issued laptops and learning apps. Parents are flocking to support efforts, like Schools Beyond Screens and the Distraction-Free Schools Policy Project, to vet and limit school tech.

At least 10 states, including Kansas, Vermont and Virginia, have recently introduced bills to restrict students’ screen time, require proof of safety and efficacy for school tech tools or allow parents to opt their child out of using digital devices for learning. And Utah recently passed a law that would require schools to provide monitoring systems for parents to see which websites their children had visited — and how much time they spent — on school devices.

Some parents are particularly concerned about YouTube, saying the platform has steered children to inappropriate videos on school devices. Gov. Gavin Newsom of California, a Democrat, recently expressed concern that one of his school-age sons had watched YouTube videos of manosphere podcasters on his school laptop.

“It was his school device,” Mr. Newsom said during a podcast interview this month. “It was YouTube. It was the Chromebook and all these algorithms.

Google said it provided tools for schools to lock students’ Chromebook screens, restrict the content they saw, manage their YouTube access and disable Chromebooks after school hours. The company said it also turned off YouTube by default for K-12 students with school-issued Google accounts.

In a small town surrounded by wheat fields, McPherson Middle School serves sixth through eighth graders in a red brick schoolhouse built in 1938. In science class, eighth graders sit at vintage lab tables next to cabinets brimming with old microscopes. The school auditorium still has its original wooden seating.

“We already have a little bit of an old-school vibe for sure,” said Ms. Esping, now in her fourth year as principal.

She is also revisiting years-old school tech decisions.

In 2016, as part of the national trend, administrators at McPherson decided to buy a $225 Chromebook for every middle schooler. Google had introduced the low-cost laptops five years earlier, with a pitch that the tech would help equalize learning opportunities and equip students with vital career skills.

“The individual use of Chromebooks is a way to empower students to maximize their full potential,” the middle school’s device policy explained in 2016.

School leaders were enthusiastic.

“The general idea was: Students are going to be more engaged because it’s online — and how exciting for them!” Ms. Esping recalled.

To capitalize on the Chromebooks, the middle school invested in online textbooks and learning apps. But administrators, parents and students found that some of the platforms seemed too gamelike or did not work as advertised.

The coronavirus pandemic only increased school reliance on tech tools. In 2021, Chromebook shipments to schools more than doubled to nearly 16.8 million, compared with shipments in 2016, according to Futuresource Consulting, a market research firm.

When Ms. Esping took over as principal in 2022, she worried that rampant tech use was hindering learning. So the school banned student cellphones.

Online bullying and disciplinary incidents quickly decreased, she said. But online distractions continued.

Some students became so hooked on playing video games on their Chromebooks that teachers had difficulty getting them to concentrate on their schoolwork, administrators and teachers said.

Students also sent mean Gmail messages or set up shared Google Docs to bully classmates with comments. Hundreds of children logged on to Zoom meetings where they made fun of their peers, teachers and students said.

The school blocked Spotify and YouTube on school laptops. Then administrators stopped students from messaging one another on school Gmail.

Even then, some educators said they were spending so much time policing student Chromebook use that it was detracting from teaching. Some parents complained their children were spending hours playing video games on their school-issued devices.

Although the idea of taking back students’ Chromebooks seemed unorthodox, given U.S. schools’ deep reliance on Google’s sprawling education platform, the middle school went ahead. The changes took effect in January.

On one recent morning, school formally began with the Pledge of Allegiance, broadcast over school loudspeakers. Homeroom teachers then led group sessions on organizational and interpersonal skills to help children navigate life without their own laptops.

Homeroom topics have included tips for students on using paper planners for school assignments and doing homework during school hours. (Students who want to practice things like extra math problems online can borrow Chromebooks from the school library to take home.)

Teachers have also taught students how to play board and card games like Scattergories and Uno.

The new laptop minimalism has also changed core courses.

During a recent English class on writing thesis statements, Jenny Vernon, the teacher, gave seventh graders a choice. They could answer questions by hand on bright salmon-colored paper or use a class Chromebook. Most students chose the paper.

In a sixth-grade lesson on fractions, a teacher asked the class to convert three-twentieths into a percentage. Students each worked on the problem on small dry-erase boards. They balanced the boards on their heads to indicate they were ready to be called on.

Computer science classes promote purposeful tech use. In one recent lesson, students used Chromebooks to program sensors and LED lights.

“It’s coding the physical world,” said Courtney Klassen, the computing teacher. “It’s not just staring at the screen.”

Some students have welcomed the changes.

Jade LeGron, 13, said curtailing Chromebooks had been “super beneficial” because students had stopped fighting with teachers over video games and had less opportunity “to be mean to each other.”

Sarah Garcia, also 13, said spending less time online had prompted students to talk more. “Since we don’t have our Chromebooks in front of our face,” she said, “most people now interact with their, like, peers and stuff.”

The school is part of a trend. In Wichita, Marshall Middle School is trying “tech-free” Fridays. In January, the Kansas Senate introduced a school device bill that would prohibit laptops and tablets in kindergarten through fifth grade — while restricting device use for middle schoolers to just one hour during the school day.

Schools like McPherson say they are not just curbing Chromebooks to reduce children’s screen time. They are also aiming to refocus learning on child development, student-teacher interactions and old-fashioned fun.

“They’ve learned how to make darts again!” Ms. Esping exclaimed, pointing up at a student-made dart jutting out from a school hallway ceiling. “They are going back to the old ways of being ornery.”

If you know me at all, you know I love food. I mean, I love food. I love eating it, I love making it, I love learning about it, I love eating it (I said that twice). Like dance, like reading and writing, it is one of the main joy-bringers of my life. As with dance, as with reading and writing, I get high on it. Figuratively, you understand; I’m not talking about special brownies. I am actually, though, talking about regular brownies, or a sourdough pizza with burrata, or—now I started thinking about cheese—parmigiano reggiano, you know the kind I mean, the stuff from Modena that Massimo Bottura says runs through his veins (I believe him). Dance, literature, art, whatever it is for you, it could be anything, swimming, travel, whatever: these things we do that make our eyes light up, or occasionally even tear up, that we look forward to and turn to, and that transport us. We engage with them and think, “Fine. Whatever kind of tedium or misery or worse is happening out there, it’s FINE as long as there is this in the world”—really good food is one of those things for me. It is one of those things that has resonated throughout my life, and at times left me in certain ways, and come back.

You know those things? Whatever it is that speaks to your soul (you will note the impossibility of speaking this way about the artifice of artificial intelligence, as souls are one of many things machines don’t have) and called to you as a child, and you couldn’t explain why. You may have followed that spark all the way into a career, or you may not even have cause to ever pause and remember it, or maybe you traveled a path somewhere in between. When I was a child on a health store visit with my dad, I didn’t know why it felt so meaningful to hold the jar of honey with the comb still in it; he told me it came straight from the hive, but I’d never seen it in the jar before. It was something alive and mysterious. I didn’t know why, baking cookies with my mom, I felt such wonder watching the flour mix into the rest of the batter, the moment it turns into an actual dough and you can see it happen. And you’re about a second away from grabbing the beater (we called them lickers!) and licking every last dollop off of it. It was something created and transformed. I didn’t know the meaning in these moments. But I knew there was a deep-running joy there. Wonder and mystery—human experience, embodied delight.

It’s been so fun to see these particular aspects of my childhood come back around. I did not become a cook, or any kind of culinary professional, so it’s a beautiful thing to find a meandering return to these sustaining practices, not just in devouring the results, but in creating them. And to remember that they always meant this much, and to give words to that. There’s a converse way of stumbling into deep meaning, one that isn’t holding on, remembering, and revisiting, but the opposite: letting go of things that meant a great deal to you and making room for something entirely new. Perhaps you were a swimmer or a surfer, and loved it and at some point got injured and had to let go of it. It doesn’t matter what the thing was, you see. Perhaps you thought of food as one of your main anchors, and at some point your gut biome forced a change so drastic that you had to completely revamp your entire frame of mind around it, which was frightening and saddening, because the things we really love become avenues for meaning and identity, for better or worse. And then, perhaps, one day, you found a return. You caught a wave of a different kind, in a different ocean, or taught someone else to. You found that some of the things you had loved most and thought you might never have joy from again came back to you in the most surprising and fulfilling ways, because of that very journey. I suppose there is a profound message here about pain and letting go, and how we can’t see the whole picture all at once, and the idea that, like everything human, holding on and letting go are not opposites but are different sides (or guides) of the same coin, leading us ultimately toward a central path.

These are more ideas that a machine never has to worry about.

I wanted to write about food in this way, both as giver of meaning and as a symbol of growth between childhood and adulthood in my own life, a forsaker and a returner, because I think food is one of the best vehicles we have to talk about being human. In this truly terrible technological moment—I am not talking about advances in healthcare and lifesaving breakthroughs; I am talking about any kind of machine activity that uses language with a pretense of humanity—in this truly terrible technological moment, human activity is the only activity worth engaging. To be human has always been sacred. To do the things that make us human now is doubly sacred: in and of themselves, and as acts of recognition and awareness of that sacredness. Make sourdough: as an act of love for your body and your friends and family, and as a remedy against the ills of non-embodied life. Yes, I think that making a loaf of bread from scratch is part of the fight against every “AI Overview” that shows up on your screen, working to make you stupider, lazier, less whole, more one-sided, less active and curious and ready for struggle, and, above all, less capable of deploying your own self-sustaining and real-world community-given powers.

Perhaps you have let go of what used to bring you joy and found new joys. Perhaps you have circled back around, with delight, to something that always carried a spark for you. “The end of all our exploring / Will be to arrive where we started / And know the place for the first time,” said T.S. Eliot, in his characteristically shiver-sending way. Or maybe you’re at neither point at the moment. To be human is to long. To be human is: to eat, to think, to feel, to create, to move, to suffer, to love … to long. To be involved in a consciousness, a singularly meaningful one, that belongs only to us yet relates us to the vast cosmos. To be aware, sometimes agonizingly, sometimes exhilaratingly, of our existence in time. “‘In fact … you’re claiming the right to be unhappy,’” Aldous Huxley famously recognized of the poor little humans in Brave New World. All of this, and more. We have to remember that the things that really make us human, the deepest points of definition here, that soul we mentioned above, are the ones we will never be able to say, because they are mysteries. And mystery, and therefore meaning, is the opposite of machine—machine, which is, in the most literal sense, dehumanizing.

We know what a machine, any machine, is for, because we made it. We don’t know what we are for. That’s it, friends. A mystery in our telos (in our ultimate end and purpose, because of the mystery in our beginning), which no matter how hard the bots try to explain, they will never, thank goodness, be able to. Therein lies the whole point. To be aware fully of the point would, in part, remove it. “Our true home is wilderness, even the world of every day,” the great American philosopher Henry Bugbee sought to remind us, with no small degree of gratitude. You can’t know why you’re here, in the larger sense, as much as you hunger to. This hunger: impossible for a machine. But you can know some of those smaller points of meaning, the ones that speak to you, the ones that feed and nourish you.

And you know you have to eat.

Every week, as AI’s capabilities and influence grow, the number of articles and people disgusted with it grows, too. This disgust has taken too long (it should have started over two decades ago when Facebook weirdly dropped into college life), but it’s a trend we have to sustain. I do not want a sense of “digital belonging,” as the workplaces frame it these days. In fact, I think this phrase should frighten anyone with eyes and ears and a brain. The place we need to belong is Earth, where we live in the flesh. The question is no more complicated and no less terrifying than whether you want to be a human or an avatar. Paul Kingsnorth, God bless him, has brought us “Artists Against AI,” “Writers Against AI.” We know that whatever AI can do, in the end, doesn’t matter at all. Only being human matters, and a machine will never be human because it cannot sustain mystery, by definition. And it cannot sustain meaning. It cannot sustain at all, because it is divorced from the very idea of sustenance. So I’ve started thinking that Food (real food) is Against AI, as well. Make food. Feed your body. Feed your cells and your soul. The mystery of a sourdough starter, fermenting, decaying in order to create a new rise, or of the perfect solution surrounding the honeycomb, both from the Earth, where we live—start there.

Image Credit: Jehan Georges Vibert, “The Marvelous Sauce” (c1890)

There’s a principle of learning that I find important but also hard to articulate. It sounds something like, “we learn what we think about, so we should get students to think hard about important ideas.” Or, “learning is more durable when students think about the deep structure of a concept rather than its surface structure.” Or, “learning works best when students make connections between concepts and think about the meaning of what we want them to learn.”

One term cognitive scientists use to describe this type of thinking is “elaboration.”

The vast majority of teachers I’ve met understand this idea intuitively. We want students to think about big ideas. Walk into a random classroom and you might find teachers asking hard questions, prompting students to dig deeper.

A Non-Example

The hard part of elaboration questions isn’t the questions themselves. The hard part is getting every student thinking about the questions.

Here is a non-example.

One practice I’ve seen recommended in this general direction is for students to create their own problems. Teach a topic, then ask students to write their own problems. Maybe they trade with a partner, or we write them on the board and solve them together. The logic looks like this: generating problems involves deeper processing than solving problems, so it will cause students to think more deeply about what they are learning.

That’s probably true! Here’s the catch: when I’ve used this strategy I find that some students are able to generate their own problems, while others stare at me with blank expressions unsure of where to start.1

There are two key qualities of a good elaboration question: first, it should require effortful thinking. Second, it should cause as many students as possible to do that thinking. Below are some examples of my favorite elaboration questions. These are my favorite because, in my experience, they are the best questions for getting reluctant students to engage with this type of thinking.

Elaboration Questions

Find the pattern

Give students a sequence of problems with some sort of repetition that reveals a pattern:

• Find 10% of 200.

• Find 25% of 200.

• Find 3% of 200.

• Find 4% of 200.

• Find 6% of 200.

• Find 60% of 200.

• Find 80% of 200.

Then ask: what patterns do you notice?

We can talk about the patterns, we can make predictions about new problems using those patterns, and we can extend the pattern in different directions — maybe finding percents of 300 or 400 to see what happens next. There’s a ton of math here. Students are practicing percents, while also creating an opportunity to think hard about why percents do what they do.

Expansion

Here is a sequence of problems that get gradually harder:2

• 5 pounds of cheese cost $50. How much does one pound cost?

• 2 pounds of cheese cost $8. How much does one pound cost?

• 3 pounds of cheese cost $11.40. How much does one pound cost?

• 3 pounds of cheese cost $12. How much does one pound cost?

• 3 pounds of cheese cost $6. How much does one pound cost?

• 2 pounds of cheese cost $6. How much does one pound cost?

• 1 pound of cheese costs $6. How much does one pound cost?

• 0.5 pounds of cheese costs $6. How much does one pound cost?

• 1/2 of a pound of cheese costs $6. How much does one pound cost?

• 1/3 of a pound of cheese costs $6. How much does one pound cost?

• 2/3 of a pound of cheese costs $6. How much does one pound cost?

The goal here isn’t that every student solves every problem. The goal is to increase the difficulty gradually, figure out where students get stuck, and use that information to help students learn something new. I often find that when I sequence questions in this way, students can solve much harder problems than if I just throw out the problem cold.

What If?

Students solve a problem, or we solve a problem together. We leave the solution visible, on paper or on the board.

Then I ask what if — here’s an example.

We solve the equation: 2x + 1 = 11.

What if the equation was -2x + 1 = 11?

The exact questions here will depend on what your students know and don’t know. This is a great way to get students thinking hard if they’re reasonably confident with the first equation, confident multiplying negatives, but don’t have much experience with negatives in equations.

Non-Examples

Students solve a problem, or we solve a problem together. Then I ask a question that is a deliberate contrast with the last problem, where students might overgeneralize a rule.

First question: distribute

2(3x + 10y - 5z)

Next question: distribute

2(3x + 10y) - 5z

A non-example helps to avoid the problem of going on autopilot, copying an example without considering the structure of that example. It often leads to great conversations!

Stepping Back

The last few examples of elaboration questions all have something in common. They are all designed to get students solving one problem or several problems accurately before doing some deeper thinking. That’s a great general strategy to get students thinking hard about elaboration questions: build confidence with a few things students know how to do, and then use that confidence as a springboard to tackle tougher questions. The details here will depend on your students. The initial questions need to be ones students can solve confidently, and the leap you’re asking students to make needs to be accessible but not too easy.

Ok, back to a few more examples.

Sentence Completion

Writing in math class can feel like a pain. I could say a lot more about this, but for this post I have one go-to teaching move to get students writing about math.3 Here’s what it looks like:

Complete the sentence:

To find the surface area of the prism, Lin found the areas of the three rectangles, added them, and multiplied by 2. She multiplied by 2 because…

This is one of those things students end up doing without really understanding. “Oh yea, I multiply by two for those problems…” Sentence completion is the best way I’ve found to get students thinking and writing about the why, without getting a bunch of blank stares and blank papers in response.

Numberless Word Problems

Word problems are a pain. This could also be a much longer post. But the short version: a good way to get students thinking hard about word problems is to take away the numbers. Without numbers, students can’t just grab numbers, smush them together with an operation, and move on. You can do this by covering up the numbers in a problem you already use, or writing your own. An example might look like this.

A runner jogs one lap around a circular track. How far does the runner jog?

Then, prompt students. What do you need to know to answer the question? What would you do with that number if I gave it to you? What if I gave you the radius instead?

Elaboration Questions

You can call these whatever you like. I’m partial to “elaboration questions” because it’s concise but you might like something different. You also might have lots of other strategies that work well! Let me know what works for you.

The key idea I want to emphasize: the goal of elaboration questions is not to get a handful of students thinking hard. The goal is to get every student, or as close as possible, thinking as hard as possible. Some of the examples I gave might seem simple. That’s often the case! We are teachers. We know a lot more than our students. It’s easy to overestimate what students know, ask a bunch of hard questions, and get a lot of blank stares in response. My priority when I ask elaboration questions is to get as many students thinking as I can. These strategies are the best tools I’ve found to do so. Even questions that seem simple on the surface can work well when we scaffold and sequence them in ways to get every student thinking.

A first-grader asks an AI chatbot why the sky is blue and takes the answer at face value. A high-schooler scrolls through a social media feed of takes on a current event without much framework for sorting the factual from the manufactured. Both scenes share the same pattern: a generation growing up on fluent, confident-sounding information, with no working practice for deciding what to trust. By the time the first-grader is old enough to vote, the pattern will have been reinforced for more than a decade.

Every information shift demands a new literacy. Twentieth-century kids learned to tell the news from the commercials, and to recognize a reporter’s byline as a different kind of authority than a pundit’s opinion. The internet brought a harder task, because anyone could publish anything, and a slick-looking website wasn’t automatically a trustworthy one. Social media layered an invisible filter on top, since the content reaching a kid had been sorted and shaped by systems optimizing for engagement long before anyone chose what to look at. AI is the latest turn of this arc, and it collapses the task further: a chatbot answer arrives confident and without citations, potentially with errors nested inside well-formed sentences and no visible trace of who produced it or how. So for a kid without evaluation skills, a news article and a viral post can land with roughly the same weight, and the cost of failing to tell them apart can have significant consequences as they get older.

Kids who grow up practicing evaluation skills enter adulthood able to meet whatever new claim shows up in front of them and hold it against a working understanding of how information gets made and whose interests it serves. They can disagree with the people around them because they’ve traced arguments back to their origins, and they’re better equipped to update their thinking when the evidence calls for it. What they then carry into adulthood is the ability to form their own opinions rather than inherit someone else’s. That ability shapes everything downstream of it, from the choices they make about their health and money to the votes they cast and the people they trust.

What we mean by information literacy

Library science has been developing evaluation skills for well over a century, and the field has a formal framework for teaching them. In 2016, the Association of College and Research Libraries published the Framework for Information Literacy for Higher Education. The framework replaced an earlier set of teaching standards from 2000 that had been organized around a checklist approach, where students learned to locate sources and evaluate them against fixed criteria.

The new framework moved toward a set of “threshold concepts.” A threshold concept is the kind of idea that reorganizes how someone thinks about a whole field once they grasp it. Learning about compound interest, for instance, permanently shifts how a person reads any decision involving money over time, in a way that wouldn’t have been available before the concept landed. The framework identified six such concepts in information literacy and called them “frames,” each describing a dimension of how information functions in the world. A kid who’s been taught the frames doesn’t just know the procedural steps of evaluating a source; she sees information differently, which is what carries her across whatever new tools and contexts she’ll meet over a lifetime.

The framework was written for college students and their librarians. But the frames it describes apply just as well to the information environments kids are navigating at home and in K-12 classrooms. One librarian practice sits upstream of the frames, shaping every information interaction that follows it: the reference interview.

The reference interview

At library reference desks, the question someone asks out loud is almost never the question they really need answered. A student might walk up and ask for “something about the French Revolution” when the material they need is something more specific, like a study of women’s political organizing in late-eighteenth-century Paris. Answering the literal question would send them in the wrong direction, so librarians developed a short protocol for surfacing the real question first. That protocol is called the reference interview.

The interview uses a handful of specific techniques:

• Open-ended follow-ups that invite the patron to say more about the underlying project

• Questions about what the patron already knows, and what they plan to do with the answer once they have it

• Restating the question back to check that it’s been heard correctly

A competent reference interview can turn a vague question into a sharp one, and that sharper question is what makes the rest of the research succeed. Without it, hours can be lost following a poorly-framed question down paths that were never going to answer what the patron actually needed to know.

The same practice transfers to how kids interact with any information source, including school research and AI chatbots. Teaching kids to interview their own questions before consulting any source is one of the most useful habits information literacy can build. A kid who learns this practice young carries it into every research task they’ll do, and the small early work of clarifying a question saves substantial wasted effort on vague or misguided answers later.

In the home: This move matters most right before your kid types into a chatbot or search engine, since these tools produce confident-sounding answers to vague questions just as readily as to sharp ones. Next time you’re about to look something up together, pause before either of you starts the search. If your kid asks “what’s the best video game console,” try responding with “best for what kind of games?” The question that comes back is usually one with a more useful answer. With a younger child wondering why sharks are dangerous, asking “what are you trying to figure out about them?” can surface what they were really after. Doing this with your kid gives them a model they can draw on later, when they’re consulting a chatbot or search bar without anyone next to them.

In the classroom: Before any research assignment, pair students up and have them interview each other’s topics, with one student playing librarian and the other playing researcher. A short exchange of this kind reliably turns a vague prompt into better research questions. The habit transfers to how students interview their own questions when they’re working alone, which is where the exercise has its biggest effect across the course of a research project.

The six frames

The reference interview clarifies what’s actually being asked before any source gets consulted. The six frames that follow do the next layer of work, which is evaluating what those sources contain once the question itself is clear. Each frame names a different dimension of how information functions in the world, and each one comes with its own way of reading sources that holds up across formats, platforms, and tools.

1. Not every expert is an expert on everything.

Framework name: Authority Is Constructed and Contextual

Authority isn’t a permanent quality a person has. Specific communities grant authority for specific claims, and that authority rarely travels smoothly across domains. A cardiologist has real authority on heart disease, but that authority doesn't extend to questions about mental health treatment, even though she holds a medical degree. A celebrity endorsing a wellness product brings name recognition to the product, but name recognition isn’t the same as knowing whether the product actually works.

This frame replaces the flat question of whether a source is trustworthy with the sharper question of what a source is trustworthy on. AI output complicates the picture further, because a chatbot answer carries the tonal texture of expertise without coming from any particular expert. Kids who learn this frame young develop the habit of asking what is this person trained in, and does this claim fall inside that area?

In the home: When your kid says “my teacher said...” or “the doctor said...” take the opening to ask a light contrast question. You can say “Does your dentist know a lot about teeth? What about building rockets?” The contrast makes the point without lecturing: expertise is specific to the area someone trained in. Over time, your kid can begin to ask on their own whether a source is speaking from their area of training or reaching beyond it.

In the classroom: Give students a short piece of writing where a credentialed person makes several different kinds of claims. An op-ed by a famous doctor on a political issue works well, as does a business executive writing about a scientific topic. Have students mark which claims fall inside the writer’s training and which reach outside it. The exercise demonstrates that credentials don’t transfer automatically across domains, and the question of where someone’s authority ends becomes one students can ask of any source going forward.

2. How information gets made shapes what it can tell us.

Framework name: Information Creation as a Process

A peer-reviewed paper and an AI answer can both claim to be sources of knowledge, but they were produced by radically different processes; that process shapes what kind of knowledge each one can hold. A peer-reviewed paper takes months or years to produce, and it passes through multiple rounds of expert review before it gets published. A chatbot answer takes seconds to generate, with no human editor involved at the moment of writing. Both arrive at a reader looking like information, but only one was shaped by a process designed to catch errors.

This frame teaches kids to ask how a piece of information was produced before deciding how much to trust it. A peer-reviewed article has been through checks that catch errors, so a kid can lean on it more heavily. A chatbot answer or a social media post has been through no checks at all, so anything that matters in it should be verified against a source that did go through verification checks. The habit we’re learning is how much to trust a source in proportion to how carefully it was made.

In the home: When your kid quotes something to you, take the moment to ask where they got it. “Did you read that in a book, hear it in class, see it on YouTube, or get it from AI?” The answer decides how much work the claim still needs. A fact from a textbook has been through some kind of review, which catches basic errors even though it doesn’t catch things like motivated omissions or framing choices. A chatbot answer hasn’t been through any review at all, so anything important needs a second verifiable source before the family treats it as true.

In the classroom: Pick a topic students are already researching, and have the class find two kinds of sources on it: an AI answer and an encyclopedia entry or textbook chapter on the same question. Ask students to infer what they can about how each source was made, based on the kind of source it is. From there, they can calibrate their trust: lean on the more carefully made source, and verify anything important from the less carefully made one.

3. Behind every piece of information, somebody wants something.

Framework name: Information Has Value

Anything created for an audience came into existence for a reason, and that reason shapes the content in ways that aren’t always obvious from the surface. Advertisements are built around the goal of converting viewers into customers, which determines the kinds of claims they make and the kinds of evidence they offer. Political messaging follows the same logic for a different end, shaping content around the goal of moving an audience toward a particular position. AI has added a new dimension of this issue, since many of the major models were trained on copyrighted material without compensation or consent, and what those models now produce serves the commercial interests of the companies running them.

The frame teaches kids to ask what the maker was after when they made the piece. The answer changes what shows up in the final content and what gets left out, which means noticing the maker’s motivation is where most of the evaluation work happens. Kids who grow up reading information this way develop a default question they can ask of anything: who benefits when I take this at face value? Motivation is one of several dimensions of this ACRL frame, alongside questions of attribution, access, and whose voices get heard; it’s the dimension most directly applicable to the content kids encounter in everyday life.

In the home: When your kid wants to watch a free video or play a free game, ask them “who do you think is paying for this to exist?” Whatever they answer, the question opens a conversation you can come back to: free content always has a cost attached, and the cost just gets paid in something other than money. For a free video, your kid is paying with attention that the platform sells to advertisers. For a free game, they’re paying with time and data, and often with temptation to buy upgrades inside the game. The same approach surfaces motivations beyond money in any other kind of content. Naming this out loud, repeatedly, builds the question your kid will start asking on their own about anything that reaches them: who’s getting something out of me reading or watching this?

In the classroom: Pick a piece of content the class has seen, and spend twenty minutes tracing what the maker wanted from the audience. The incentive might be commercial, where an advertiser or sponsor is working to drive sales, or it might be persuasive, where a campaign or organization is working to shift opinion on an issue. The exercise puts the question of motivation in front of students directly, so they can practice reading content for the purpose driving it rather than only for what it says on the surface.

4. Good questions get sharper as you ask them.

Framework name: Research as Inquiry

Research doesn’t happen in a single act of looking something up. A question goes in, partial answers come back, the question gets refined based on what those partial answers reveal, and the cycle repeats. Meaningful questions rarely have clean single answers, and researching well includes the capacity to stay with a question while it sharpens rather than collapsing it too early into a premature conclusion.

AI chat trains the opposite reflex: a chatbot offers a single finished answer to a single question, and the interaction feels complete as soon as it ends. Kids who only practice that pattern miss the underlying skill of refining questions as they learn more. The skill is what separates researching a topic from simply collecting answers.

In the home: When your kid asks you a question, try bouncing it back first: “What do you already think about that?” or “How could we find out together?” These simple questions invite your kid into the thinking process rather than handing them a finished answer. Over time, children can develop their own opinions and their own research instincts, instead of waiting for the adult in the room to tell them what’s true.

In the classroom: For any research assignment, build in two checkpoints where students rewrite their research question based on what they’ve learned so far. By the end of the project, students can compare their final question to the one they started with and see how it changed. The point of the exercise is to make the iterative nature of research visible, so students experience research as something that reshapes the question itself (and not just a hunt for answers to a fixed one).

5. No serious idea stands alone.

Framework name: Scholarship as Conversation

Substantive ideas rarely get developed by people working in solitude. They emerge from groups of people thinking and writing in response to each other over time, with each contribution shaped by what came before it and shaping what comes after. Making sense of something means recognizing the larger argument it's a part of, including the earlier work it's reacting to and the work it will provoke in turn.

The frame shows up vividly in academic writing, where literature reviews and citations make the conversation visible on the page. The same pattern operates in journalism that builds on earlier reporting and in political writing that engages older arguments. AI-generated summaries do something more concerning, because they compress many conversations into a single confident-sounding voice that erases the evidence a conversation ever existed.

In the home: When you’re reading a book or watching a show with your kid, point out the moments where the book or show is reacting to something else. You can say things like “This story is making fun of those old fairy tales we read last year” or “This creator made a video to argue with what that other person posted last week.” Comments like these position the book or show as part of a larger dialogue rather than as a standalone pronouncement, introducing the question a kid can carry into anything they read or watch: what is this responding to or building on?

In the classroom: Take any text students are already reading and teach them to look at its bibliography or works cited section. The simplest question to ask is who the author is building on or arguing with. Walk the class through a single citation chain: pick one reference in the text, and look at that source together to see what it says and how the original text engaged with it. Students learn that every serious piece of writing is part of a longer, larger conversation, which can change how they read everything afterward.

6. Finding good information takes more than one search.

Framework name: Searching as Strategic Exploration

Skilled searching looks nothing like a single query producing a single result. It’s a process of trying something, seeing what comes back, adjusting based on what the results reveal about the topic and the tools, and trying again with sharper terms. The craft lives in knowing when to try again and what to vary when the first search doesn’t deliver.

AI chat has flattened this craft for many users, because a chatbot offers the surface of an answer on the first try. The pattern trains our reflexes away from iteration. Kids who grow up inside that reflex miss the underlying skill, which is the ability to move strategically through an information landscape when a first attempt doesn’t give them what they need.

In the home: When your kid watches you look something up, show them that you try more than one search. Say out loud when a search doesn’t give you what you need, and try a different approach. You can narrate it directly, with something like “Hmm, that didn’t work, let me try different words,” or “this site doesn’t seem reliable, let me look somewhere else.” The modeling teaches your kid that search is an iterative process rather than a single click.

In the classroom: Put several search tools in front of students and have them run the same question through each one. A library database and an AI chatbot will return noticeably different results for the same question, and comparing the differences shows students how information is structured in ways that aren’t visible from any single tool. The exercise also makes clear that no search tool gives a complete picture, because each one is designed to prioritize certain results over others. The ability to turn to multiple sources, or refine their search process over time, is the foundation to students’ verification strategies as they get older.

The arc over time

The information environment kids are growing up in now will keep shifting, and the shifts will inevitably keep coming. What makes the ACRL framework durable across these shifts is that the frames describe dimensions of information that stay stable when the tools change. Authority is always specific to a domain. Information is always produced through some process, and the process shapes what the output can carry. Every piece of information moves through systems of incentive, and every serious idea takes part in a larger conversation, whether or not the tool it arrives through makes the conversation visible.

Kids raised on these habits grow into adults who can encounter new information and truly see what’s in front of them. They can recognize when an author is reaching beyond their expertise, when incentives are shaping what gets said, when an article is in conversation with other articles, and when the first search result isn’t the last word. They can grow into voters and citizens who think the way they want to think rather than the way someone else wanted them to. This capacity gets built in the small conversations of home and classroom, long before the first vote gets cast or the first big decision needs making.

What these skills add up to has a name: information resilience, the capacity to meet any claim, from any source, without being knocked off course by what arrives. A resilient reader can stay with a question while it sharpens, and can hold her ground when the people around her are settling on conclusions faster than the evidence warrants. She has an internal sense for what’s worth a closer look, built up over years of practice. That kind of resilience is what parents and teachers can give the kids growing up now, and it will keep working long after any specific tool or platform has been replaced.

Have you read the Founding Member Report: The State of AI yet?

A comprehensive guide for information navigators who want to understand where AI is actually heading and what it means for how we find, evaluate, and use information in 2026.

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Introduction

I’ve spent the better part of the last decade studying how people actually learn. The question I keep coming back to is this: when does an experience merely feel educational, and when does it actually help someone understand enough to do something differently?

The difference is easy to miss because good experience design is persuasive. An experience can feel thoughtful, elevated, and full of intention. It can spark curiosity. It can make you want to belong. And still, it can fail at the thing that matters most: helping you understand what to do next.

That was the question I kept thinking about when I visited Nespresso’s flagship in Flatiron. The store is positioned as a destination for discovery: tastings, masterclasses, theatrical design, staff who act more like guides than salespeople. I walked in expecting something close to an interactive museum for coffee. Unfortunately, I walked out having learned very little, bought nothing, and feeling more loyal to the machine I already owned.

Here is the thing, though. I was exactly the kind of customer this store should convert because:

1. I drink coffee every day

2. I care about convenience more than craft

3. I’m a sucker for good design

4. My coffee machine had recently broken

Even more, Nespresso’s own strategic challenge, documented in an IMD case study, is attracting younger consumers to an aging customer base. I was standing in their store, credit card effectively out, waiting to be taught why their product was the right one. The flagship had one job.

What the store got right

The space itself is stunning: calm, beautifully lit, and designed with a care you can feel the second you walk in. It reminded me of the Harry Potter store in New York: that same sense that someone had designed not just a retail environment, but a world. And that instinct is sound. Falk and Dierking’s research on museum learning shows that physical context directly shapes what people notice, engage with, and retain. A well-designed space creates the kind of openness that makes people willing to explore rather than retreat.

Nespresso carries that instinct further than most. Within a few feet of the entrance, there is a self-serve coffee station. A friendly staff member greeted me, explained the blend, and demonstrated the standard machine. Before anyone tried to sell me anything, I was already holding a cup and using the product. That is smart design. It is also, whether Nespresso knows the term or not, a first step in building self-efficacy: give someone an early experience of success before asking them to do anything hard.

This is the art of customer education. It makes a person curious enough to step closer and creates the feeling that this product might be for someone like me.

But art only opens the door, and what happened next showed me exactly why that isn’t enough.

The latte machine

Right next to the standard machine was a more advanced one for lattes. Now, I love lattes, and the thought of making one at home every morning on a beautifully designed machine was exactly the aspiration Nespresso was hoping to sell. I had never used a latte machine before, but in that moment, I wanted to learn.

There was no one to ask. The staff member had moved on, and nothing else picked up the gap. No signage. No visual guide. No “start here” prompt. I pressed a few buttons, could not figure it out, and defaulted to the simpler machine.

In learning science, there is a concept called a teachable moment: a narrow window where someone is aware of a gap in their own understanding and motivated to close it. The latte machine was a textbook case. I had already cleared the hardest threshold. The store did not need to teach me everything about milk frothing. It just needed to help me across one small bridge, from curiosity to a first successful attempt. A laminated three-step card would have worked. A QR code to a thirty-second video would have worked. Instead, the window closed, I shrugged, moved on.

That is the thing about teachable moments: they do not wait. The gap between “I want to try this” and “OK, I guess I cannot” is seconds, not minutes.

The aroma station

The latte machine was a failure of support at the point of need. What happened next was a different kind of failure: a missing pathway from discovery to action.

Near the machines was a perfume-style aroma station where you could smell different coffee profiles by pressing a pump. It was clever, theatrical, and I loved it. I found a profile that felt like exactly my taste.

I looked up to figure out what to do next. There was a wall of capsules at the very back of the store, but nothing connecting the scent I had just discovered to any specific product on that wall. No sample pack. No card with the blend name. No small sign saying: if you liked this, start here. I was standing there holding a preference I had learned thirty seconds ago, with no way to act on it.

The store helped me discover a taste but gave me no pathway from that discovery to a decision. Desire without understanding or confidence is just a nice memory.

What this visit taught me

These two moments are different failures, but they point to the same structural gap. In both cases, the store created a compelling experience and then quietly asked the customer to bridge the hard part alone.

That gap is what clarified something I have been thinking about for a while. Customer education is not one job. It is four, and they are sequential.

First, experience. The customer interacts with the product in a low-stakes, inviting way. Nespresso does this beautifully.

Second, understanding. When the customer hits something unfamiliar, support appears at the moment of need, in the format that fits the moment. This is where the latte machine failed.

Third, confidence. The customer starts to feel, in some small but real way: I can use this. I can tell the difference. I can make a good choice. Bandura called this self-efficacy, and it is built through successful attempts, not through exposure. The aroma station created exposure without any path to a successful attempt.

Fourth, commitment. Once the customer has enough confidence, the path from interest to decision should be clear.

You cannot reliably skip from experience to commitment without building understanding and confidence in between. But that is exactly what most customer education tries to do. It creates a compelling first encounter and then hopes the customer will figure out the rest.

Why this matters beyond coffee

That pattern shows up constantly in digital products, and especially in AI. A company creates a magical first output: an onboarding flow that feels polished or a demo that makes the system look effortless. The user sees the promise immediately.

But what comes next remains unclear: how to repeat the outcome, how to make a good choice, how to trust the product enough to change their actual workflow. The experience is impressive. But, the user is still not capable.

That is the gap between the art and the science of customer education. The art creates openness. It gets someone to step closer. But the science is what carries them through: the support and sequencing that turn interest into capability.

Nespresso invested deeply in the art and left the science almost entirely to chance.

The companies that get this right will not be the ones with the most polished demos or the prettiest flagship stores. They will be the ones that understand a harder truth: delight opens the door, but capability is what closes the sale.

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Akanksha is a learning engineer who has spent the last decade working across classrooms, EdTech, and AI-enabled product teams. She writes AWU Field Notes about learning science, customer education, and what it actually takes to help people go from confused to capable.