Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IBM computer scientist Arthur Samuel about an algorithm that learned to play checkers. Chess and the Chinese board game Go are famous AI test beds too. 

Judged purely on its puzzling skills, AI is improving a lot—and quickly. In late 2024, a team of scientists from Columbia University showed that even the best models could figure out only 18% of the infamous New York Times Connections puzzles; by early 2025, some models could solve them near perfectly every time. 

But puzzles do more than just highlight the inexorable advance of AI capabilities. Seeing where models succeed and fail—and where we humans still beat them—can provide a useful window into the technology’s strengths and weaknesses. Despite advances, today’s models still fumble: Subtle changes in classic riddles often trip them up, and visual puzzles are a particular weak spot. 

Here you’ll have the chance to test your wits on puzzles that have stumped models at one time or another. Some might be as tricky for you as they were for the AI; others are so simple that they’ll have you doubting whether AI is really intelligent at all. Each one highlights at least one way in which machine and human cognition differ. If you ace the test, you’ll have proved that you can out-puzzle an AI—at least for now. 

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Spatial Reasoning

Let’s start with a domain where humans have a huge advantage: spatial reasoning. If you’ve ever taken an IQ test, you may have done a mental rotation problem. These puzzles ask you to determine whether different images represent the same objects from different angles. Though today’s language models typically have the ability to analyze visual inputs, they still fail abysmally at these puzzles. For all the talk of how world models can help AI understand physical environments, LLMs still don’t seem to be able to manipulate 3D objects the way spatial thinkers like architects and mechanical engineers can.

Mental Rotation

Instructions: Choose the answer that shows the object in the prompt, but from a different angle. In each case, there’s only one correct answer!

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Memory & Adaptability

Frontier LLMs have extraordinary memories; they were exposed to a monstrous volume of facts during training and can recite many of them faithfully. That’s an asset for outcompeting humans at trivia, but it can also be a liability. When a puzzle closely resembles one a model saw during training, the model may whiz by key differences and respond with what it memorized. 

This held true in a 2024 study in which researchers from Google and the University of Illinois Urbana-Champaign trained and tested models on slight variations of a classic type of puzzle called Knights and Knaves. In these problems, some characters always tell the truth and others always lie, and you have to figure out who’s who. The same principle may be at work in a test called SimpleBench. These questions resemble more complicated problems that models likely encountered in training. Humans spot the trick, but even top-tier models trip.

Knights and Knaves

Instructions: The only thing you need to know to solve these puzzles is that knights always tell the truth and knaves always lie. Determine who’s what on the basis of what each character says.

SimpleBench

Instructions: Read these SimpleBench problems carefully, and you should be able to figure out the answers in no time.

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Abstract & Visual Reasoning

AI doesn’t just bungle visual problems in 3D—two dimensions can trip it up as well. That’s a major factor in how well models do on the most famous ­puzzle-based benchmark, ARC-AGI. These problems require you to infer abstract, general rules from a set of examples. Models do better on ARC puzzles when they receive each grid not as an image but as a string of numbers that encodes the color of each cell. 

Research suggests that even when models answer ARC-AGI questions correctly, they often do so using byzantine and non-­generalizable rules, whereas humans draw on simple visual concepts. Despite these disadvantages, models have gotten quite good at ARC-AGI over the past year, but some puzzles—such as the one printed here—still stump them.

ARC-AGI

Instructions: Study the three pairs of grids shown below to figure out the rule that dictates how the ones on the left transform into the ones on the right. Then get out your markers or colored pencils and fill in the fourth grid using that rule. (The solution is the same no matter which way the grids are oriented.)

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Intuition

It’s not just AI models that fall into traps. We humans have our own cognitive foibles, many of which AI does not share. Psychologists have designed problem suites that invert the SimpleBench phenomenon: For these questions, humans often give knee-jerk answers, whereas models will respond deliberatively. Some of the problems exploit errors in the ways that we intuitively do math; others are phrased so as to suggest obvious answers that fall apart if the question is read carefully. 

Lightning Round

Instructions: Answer the questions below as quickly as you can.

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Increasing Complexity

In some cases, whether an LLM can complete a puzzle is a matter of scale. One study from researchers at Apple found that LLMs can ace simple versions of the Tower of Hanoi problem, which involves moving a stack of disks one at a time without ever putting a larger disk atop a smaller one, and river-crossing puzzles, in which a group of people must traverse a river according to certain rules. But only up to a point: As the number of disks or people hits six and higher, the models began to falter.

In another study, researchers at the University of Washington, Stanford University, and the Allen Institute for AI observed that LLMs struggle similarly with logic grid puzzles, which require deducing the attributes of a set of individuals from a list of clues. The Apple paper went viral, but commentators questioned whether the results reveal a unique limitation of LLM reasoning—or just that it’s normal to make errors as complexity piles up.

The River

Instructions: Using the scenario provided, plan the trips necessary to get everyone across the river. 

Logic Grid

Instructions: Using the list of clues, determine who lives in each house and what style of music each person enjoys. There is only one possible solution. You may find it helpful to fill out the grid below to keep track of your deductions.

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Grace Huckins is an AI reporter at MIT Technology Review. They have a PhD in neuroscience.

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Credits:

Mental Rotation: CC BY 4.0. Stogiannidis, Ilias, Steven McDonagh, Sotirios A. Tsaftaris. Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models (copyright 2025); illustrations by John MacNeill. Knights & knaves: Courtesy Dan MacKinnon. Simplebench: CC BY 4.0. SimpleBench Team. The Text Benchmark in which Unspecialized Human Performance Exceeds that of Current Frontier Models (copyright 2024). ARC-AGI: Courtesy ARC Prize Foundation. Lightning round: CC BY 4.0. Hagendorff, Thilo, Sarah Fabi, Michal Kosinski. Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. Nat Comput Sci 3, 833–838 (copyright 2023). The river: Adapted from Propositiones ad Acuendos Juvenes, Alcuin of York (ca. 800 CE). Logic grid: Apache License 2.0. Lin, Bill Y., Ronan Le Bras, Kyle Richardson, et al. ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning (copyright 2025)

People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. 

Now there are two. 

Four short years after the release of ChatGPT, many of us now take it for granted that we can converse naturally with our phones or computers. LLMs like Claude, DeepSeek, and OpenAI’s GPT models are fluent and flexible enough to masquerade convincingly as humans. But peek behind the computational curtain, and there’s a catch: Teaching a computer to use human language still requires an inhuman amount of data. An LLM can easily churn through a hundred thousand times more words than a person will experience in the process of mastering their mother tongue—and way more than children might hear by their first birthday, when they typically start to grab hold of language.

“The progress recently has been amazing,” Michael C. Frank, a cognitive scientist at Stanford University, says of LLMs. “But we still have to burn down a forest and scrape the entire sum of all human knowledge to re-create this milestone that happens in our living rooms over the course of a year.”

This yawning divide between children and machines is called the data efficiency gap. And it raises a tantalizing question for cognitive scientists and a challenge for the architects of AI models: How is it that kids can still outperform the most linguistically sophisticated machines ever built? 

Finding answers has stakes for both AI research and cognitive science. For the past decade, language models have mostly gotten better by getting bigger. Meta’s open-weight LLM Llama 3.1, released two years ago, chewed through 15 trillion tokens (word-like chunks of language) in pretraining—the main step of training a model that happens before it is fine-tuned for a specific task, like being a chatbot. Frontier models could be pretraining on 10 times more data, says Ethan Gotlieb Wilcox, a cognitive scientist and linguist at Georgetown University. But there’s only so much internet to train on, and eventually—perhaps as early as the 2030s—the well of easily available data could run dry. 

Kids show that it could be possible to learn more with less. Far less. A preteen raised in a linguistically rich home may have heard something in the vicinity of 100 million words. Add literacy to the mix and you can boost that word count to maybe 300 million words by age 20. 

The difference in scale is something that can only really be gestured at in analogy. “Claude has seen the amount of language that an entire city will experience in one generation,” says Wilcox. If you were to print out on paper all the words used to train a modern LLM, you could make a stack that would reach past the International Space Station. The human preteen’s 100 million words, meanwhile, would stack up just 20 meters. And we can make do with far less than that. 

By reverse-engineering the way kids learn, scientists hope to be able to create more data-efficient AI models, which could be useful for everything from training AI effectively on video to creating chatbots that serve minority language communities. Testing hypotheses about human learning in machine models could also settle enduring questions about language and children’s developing minds. Are we born with a language instinct, or would it be possible, even in principle, for a child to learn language purely from experience? Is the way we process language a quirk of our biology, or might at least some of it reflect universal constraints on how languages can be used and learned? 

The essential elements

Most of us realize language is hard only when we try to learn a new one after childhood. The past perfect tense, rolled rs and nasal vowels, the genitive case, phrasal verbs, grammatically masculine tables and feminine spoons—many are the instruments of linguistic torment for the adult language learner. It’s typically effortless to learn our mother tongues, however. Toddlers usually start producing grammatically correct sentences after hearing something like 10 million words, or 30 million on the high end. 

“It’s just totally miraculous,” says Frank. “If you train GPT-2 on 30 million words, you get a nonsense generator; you don’t get a kid.” 

Exactly how babies pull this off is a mystery. Researchers know a lot about what kids learn and how they use language at different stages in development, but there’s still a lot we don’t know. Perhaps the most enduring question is why babies can learn language at all. The syntax of human language—the rules for combining words into sentences—includes recursive, nested structures that allow us to express virtually infinite ideas with a finite lexicon of words and pieces of words. This seems like something that should be a problem for babies. They only splash about in the shallows of a fathomless ocean of language. And yet, somehow, that’s enough. From a drop, they infer the depths.

One solution, put forward in the 1950s by the MIT linguist Noam Chomsky, is that babies are born with hardwired knowledge of grammar. Chomsky was reacting to a rival view, championed by the psychologist B.F. Skinner, that language acquisition is entirely environmental. Skinner thought language was learned through conditioning and reinforcement, the way a dog figures out how to sit or shake for treats. Chomsky countered by citing the “poverty of the stimulus”—the idea that language, especially syntax, is too complex and children’s exposure to it too “impoverished” for them to learn entirely from experience. “His signature argument was, essentially, that language cannot be learned on the basis purely of statistics,” says Richard Futrell, a linguist and cognitive scientist at the University of California, Irvine. Instead, Chomsky posited that language is based on a set of logical rules and argued that children needed innate knowledge of those rules to deduce the grammar of their language from scraps of speech.

“It’s just totally miraculous … If you train GPT-2 on 30 million words, you get a nonsense generator; you don’t get a kid.”

Michael C. Frank, cognitive scientist, Stanford University

The Chomskyan view of language dominated linguistics in the US for decades under the moniker of generative grammar. And it was a major influence on computer science in the 1950s and ’60s, when AI was enjoying its first boom time and the lines between linguistics and natural-language processing dissolved in a flood of military funding; the Pentagon wanted computers that could understand English and translate Russian. 

Despite early successes of simple neural networks, which learn to recognize and reproduce statistical patterns, AI researchers in the United States largely adopted a rule-based framework influenced by Chomsky’s theories. They tried to teach language to computers by explicitly coding the rules into programs—think less immersion experience, more grammar class. This approach, part of a broader trend called symbolic AI, prevailed for decades. It also largely failed to produce models actually capable of handling human language at scale. Interest in natural-­language processing chilled in the “AI winter” that began in the 1970s. 

In the aftermath, neural networks started to make a comeback. But it wasn’t until the 2010s, when computer hardware was getting cheap and capable and the internet was getting big, that their performance began turning heads. By 2018 and 2019, the models BERT and GPT-2, which were built on a new architecture—the transformer—and trained on billions of tokens, made it clear to insiders that learning from a massive glut of data could work for language. In 2022, with the breakout success of OpenAI’s chatbot ChatGPT, it was clear to everyone.

LLMs are not brains. What they are is powerful statistical learners—naïve pattern-learning machines without any of the evolved biological quirks folded into the human cortex. In other words, they are exactly the kind of thing a generative linguist two decades ago would have thought could not learn language. And yet here they were, writing believable sonnets and passing grammar tests.

“No matter how skeptical you are about AI, the thing that everyone has been really impressed with is: These things learn syntax,” says Alison Gopnik, a developmental psychologist at the University of California, Berkeley. “I didn’t think that was going to turn out to be true. And I think most people didn’t think that you could just look at the statistics of a large sample of language and figure out grammar.”

But what about learning from a small sample of language—a child-size one, say? Is it possible to build a baby-scale model that’s anything more than a nonsense generator?

Baby talk

Alex Warstadt, a linguist and data scientist at the University of California, San Diego, remembers the years around the release of BERT and GPT-2 as a heady time. Back in 2019, he was still a PhD student in linguistics at New York University, watching his field change before his eyes. The mere fact that language models could learn English by churning through text was a challenge to prevailing Chomskyan ideas. But many linguists remained skeptical that LLMs could tell us anything about how humans acquire language. 

“I always got pushback on one issue in particular. And that was the size of the data sets of the model,” says Warstadt. “There was never a time when people were training language models at human scale where we were impressed by them.”

But Warstadt saw promise in LLMs: A scientific model doesn’t have to be perfect to be informative, and LLMs were clearly powerful simulations of human language use. By building hypotheses about how children learn into models and measuring their performance—how close they came to closing the data gap—might scientists be able to put their ideas to the test? In August 2022, Warstadt posted a Twitter thread laying out an argument that neural networks could be useful models of language acquisition. After some back-and-forth in the comments with AI researcher Leshem Choshen, Warstadt floated the idea for what would become BabyLM, an annual competition organized by Warstadt, Choshen, and several other researchers to train models on small data sets.

That was four years ago. Since then, BabyLM has added workshops and inspired spin-offs including a competition for baby models trained on Chinese. The main event challenges researchers to train language models on a “developmentally plausible” corpus of just 100 million words (for the toddler-scale track, 10 million) drawn from storybooks, dialogue, movie subtitles, Simple English Wikipedia, normal Wikipedia, and actual transcripts of speech directed at children. The models are evaluated on the kinds of grammar benchmarks that psycholinguists use with humans, says Georgetown’s Wilcox, one of the organizers.

One kind of task involves presenting test subjects—human or machine—with sentences and looking for indications of confusion or surprise at ungrammatical features. For instance, a test might compare the sentences The keys to the cabinet are on the table and The keys to the cabinet is on the table. “When humans see ‘is,’ they’re like: What? That’s not supposed to be ‘is,’ ” says Wilcox. For a human, that surprise might be measured by tracking eye movements. For language models, researchers use a measure called surprisal, which assesses how unlikely the model predicts a sentence or part of a sentence to be.

The competition has already challenged some assumptions, such as the effectiveness of curriculum learning. Curriculum learning starts with simple training data and works up to more complex inputs—a bit like starting with baby talk and getting more sophisticated over time. And it was by far the most popular approach taken in the first round of BabyLM, says Warstadt. But it didn’t work as well as expected.

“The appeal is just kind of hard to resist, you know. [Curriculum learning] seems to really line up with ways that we believe humans are learning,” says Aaron Mueller, a computer scientist at Boston University and one of the BabyLM organizers. “But it seems like these transformers don’t really need to have their data ordered in such a way to learn effectively.” 

Perhaps a touch ironically, the best BabyLM models aren’t inspired by babies at all. The 2024 champ, GPT-BERT, is a transformer trained partly to predict the next token in a sequence, like modern LLMs, and partly to act like BERT, a “masked language model” that fills in the blanks in sequences of tokens Mad Libs style. Impressively, when GPT-BERT was pretrained on about 100 million words, it was able to beat the performance of Meta’s Llama 2 70B—an LLM pretrained roughly 15,000 times that amount—on one of the BabyLM benchmarks.

Still, BabyLM models are not on the same level as LLMs. Many can’t produce text at all, and even GPT-BERT would seem clunky next to a modern commercial model. Ultimately, while they are “baby”-size, the way these models learn isn’t very baby-like. Kids are not disembodied computer programs whose only “experience” of the world comes through written text. They take in the world via their senses—especially vision and hearing. To close the data gap, some researchers think, machines will need to start learning through the eyes and ears of children.

Taking it all in

When Michael Frank started his lab at Stanford about 15 years ago, scientists didn’t really know how babies experience the world. Developmental psychologists were just beginning to glimpse babies’ lives through headcams.

“The insights that came out from that early research were that kids’ experience looks really radically different than we thought,” says Frank. “It’s much more focused: They’ve got these little short arms, so the objects are, like, right in front of them. And they live in a forest of knees.” 

Frank was excited to use headcam footage to train machine-learning models to test hypotheses about how kids learn language, but he needed more data. So he and four colleagues recruited three babies—all the children of psychologist mothers who knew what they were getting themselves into—to don headcams for science. The project, called SAYCam, recorded two hours a week of each child’s life between six months and two and a half years of age.

“[The families] were willing to release that video, and that’s critical,” says Frank. “So we released it, and people started training models on it.” 

One of those people was Brenden Lake, a cognitive scientist and AI researcher at Princeton. In 2024, when he was working out of New York University, he and his colleagues presented a model trained on 61 hours of raw SAYCam data that learned to identify objects and associate them with words. Many theories in developmental psychology propose that children need some biases to help them pick out particular parts of their raw sensory experience and associate them with bits of language. For instance, it’s thought babies assume that a new word like “shoe” refers to a whole object rather than a part of it (like a shoelace), says Lake. But the model Lake’s team built was able to learn to identify objects in the video footage and associate them with words without any such biases. “It turns out you can get a real start on language learning using a lot less than what a number of theories suggested,” says Lake. Still, he adds, “we don’t get a two-year-old out of [training] when we’re done.”

But perhaps it’s not surprising that such models can’t replicate childlike capabilities by working with a few dozen hours of footage cobbled together from short snapshots over several years of a child’s life. It could be that the shortfalls just indicate a lack of realistic data. After all, babies can’t wear a headcam 24-7; efforts like SAYCam and its successor, BabyView, record at best a few hours a week. So researchers have the choice between working with a tiny slice of the life of a single child or with larger data sets of footage pooled from many kids. Either way, a model’s training data is still a far cry from the lived experience of a child.

That could be changing. Uri Hasson, a neuroscientist and psychologist at Princeton, spent the last five years on a project to record the first 1,000 days of 17 children’s lives. The participating families wired every living area in their homes (except bedrooms and bathrooms) with cameras and microphones and recorded 12 hours a day, almost every day. The resulting data set, described for the first time in a recent preprint, is of a scale that would have simply been impossible to work with absent new AI tools for transcription and video analysis, says Hasson. “For the first time, we have the input,” he says. “It’s really only the beginning.” 

Missing ingredients

So far, training models on video has proved difficult. While text-based models emerge fully fluent (after ingesting huge training data sets), multimodal models trained on video from kids are far from that. Lake’s model, for instance, learned simple words, like “ball” and “cat.” Attempts to supplement text with visual data haven’t worked for BabyLM participants, says Warstadt. Gopnik thinks the issue could be that kids do not simply sit and watch the world go by. “Children are actively exploring, which means that they’re actively choosing their own data,” she says. “Kids are constantly experimenting.” Maybe that’s the missing ingredient. 

Research by Gopnik’s group—including studies of grade schoolers exploring a Minecraft-inspired game—shows that what looks like child’s play is in fact an effective way to learn cause and effect. Kids seek out experiences and take actions that maximize their “empowerment,” or the ability to make a predictable impact on the world. 

Unlike models, children are aware of what they don’t know and have a drive to fill their knowledge gaps, says Elizabeth Bonawitz, a developmental cognitive scientist at Harvard. And children’s social lives also help them learn, she says. Her research has shown that children interpret information differently when they know an adult is trying to teach them something. “Children are not only reasoning about the evidence they’re being told,” says Bonawitz. “They’re reasoning about the teacher, about the teacher’s knowledge, and about why the teacher is telling [them] this particular information.”

That’s very different from how models learn: passively and in isolation. Perhaps if models were built to seek out information to fill in their own blind spots, experiment with language and observe how other language users react to their babbling, and reason about some kind of simulated social world, they’d learn better. Last year’s BabyLM actually opened the competition to models that could learn by interacting with other models. But the social models didn’t outperform standard ones.

Of the leading industry labs, Meta seems the most interested in taking inspiration from kids—specifically for training models from video. Two Meta researchers were involved in BabyLM’s multimodal branch, and Meta scientists—together with academic researchers, including Frank—recently announced a benchmark and challenge for training models on baby headcam footage. Frank also says a stealth-mode AI startup called Flapping Airplanes has taken interest in his research. Neither Meta, Google DeepMind, OpenAI, nor Flapping Airplanes agreed to an interview. 

For now, frontier labs aren’t exactly racing to borrow tricks from children, says Gopnik. She thinks it’ll be the next generation of AI—whatever replaces the transformer—that will take lessons from developmental psychology.

Perhaps the most enticing reason to close the data gap is that it could help us understand ourselves.

In general, the machine-learning community is less interested in mimicking the brain than in just building something that works, says Mueller. But he thinks awareness of—and interest in—the data efficiency gap is growing. An example is the NanoGPT Slowrun benchmark, launched by Q Labs in March 2026. “They have very similar goals to BabyLM,” says Mueller. “But they’ve dropped the motivation from human language learning and really just focused on the data efficiency angle.”

One reason Warstadt wants to close the data gap is to democratize AI so that universities and others without the resources to hyperscale can train good models and stay relevant in AI research. David Samuel, a machine-­learning researcher at the University of Oslo and one of GPT-BERT’s architects, has a more personal reason to work on this problem. He’s Czech and works in Norway, and there’s a lot less data in Czech and Norwegian available for training LLMs than there is in English. Minority languages like Sami might have just tens of millions of tokens available, says Samuel—about the scale of a toddler’s exposure. “The question was,” he says, “how can we develop language models that are just as capable as the English ones for small languages?”

But perhaps the most enticing reason to close the data gap is that it could help us understand ourselves.

Bonawitz says she was initially skeptical that large language models could reveal anything about cognition. LLMs and brains are, after all, very different. Brains are embodied. Our neurons are not tidy lines of code but living cells. And our brains grow and change as we learn and age—LLMs pretrain once and never again. But as different as the two systems are, says Bonawitz, “I’m sort of revising my beliefs.” She’s been won over by the idea of studying models the way comparative psychologists might study animal minds to illuminate our own.

Researchers like Warstadt, Frank, Wilcox, Lake, and Hasson are already using language models as a kind of linguistic lab rat, an imperfect but informative stand-in for a real human language user—especially for questions that are more about learning and language and information processing than anything specific to our brains or biology. When models can do things with language we thought were impossible, it challenges old assumptions. And researchers can build hypotheses about language learning into models—say, by simulating different degrees of bilingualism or depriving models of exposure to certain grammatical forms—and test those hypotheses in a way that would be impossible to do with real children. Futrell compares the situation to teaching language to an alien and then opening up its brain to see what happened. 

While other animals communicate, only humans converse. Now there’s something neither animal nor human that can talk, too. LLMs open up the possibility for comparative studies, even if models and minds are vastly different. “For the last 100,000 years or however long human language has existed, humans have been the only entities in the universe that use language. Now there’s this other linguistic entity,” says Warstadt. “Finally we have a model; not in the sense of a language model, but in the sense of a model organism.” 

Elise Cutts is a science writer based in Austria.

Sometime in the late 2000s, Pim Sullivan-Tailyour was sitting in the back of a car, headed toward her great-grandmother’s tiny town in the south of Thailand. She watched big mountains pass by out the window. She was just six years old but was about to be hit by an adult-size realization. “They were just quarried out,” she says. “Like half the mountain just dug out and disappeared, and so there was just this huge orange face.” 

Although she probably didn’t know the word “quarry” back then, she sensed that what she was seeing was unnatural. She also knew, from family stories, that her mother had bathed in a nearby river when she was young, its water so glass-clear she could see fish swimming by. But the extraction had muddied the water. “We’re changing things,” Sullivan-Tailyour thought. “And it doesn’t seem right.”

It was the first time it occurred to her that humans could alter the world—and that they were. For the worse. 

She carried that knowledge with her as she got older and her family moved to the UK. There, she joined teenagers from other schools in the Schools Sustainability Network, an umbrella organization for groups that work on environmental initiatives. 

But even with those connections, Sullivan-Tailyour didn’t encounter anyone at her own school who was interested in environmental activism. She felt lonely in her desire to push for change—and burnt out on working solo. “I was pretty much the only person who wanted to do these things,” she says. 

That was when she heard about another initiative, called Force of Nature. A UK-based organization, in 2021 it had spun up Becoming a Force of Nature, a kind of informal online group therapy program, for kids and young adults worried about climate. The sessions aimed to help young people like Sullivan-Tailyour take their worries and turn them into action, stemming the exact kind of anxiety and burnout she was feeling.

She signed up and soon logged into Zoom for her first session. Dozens of faces from dozens of countries looked back at her. “I realized that I wasn’t alone,” she says. 

She began to suspect that kids at her school actually did feel the way she did, even if they weren’t doing anything about it. 

Sullivan-Tailyour was right, it turns out: Global surveys have found that most kids are anxious about the state of the world—and not just about whether sea levels and carbon counts will continue to rise. They are growing up in a time of what some historians, policymakers, and economists are calling a “polycrisis”: The planet is warming, yes, but also a pandemic happened and could happen again, conflicts keep erupting, nuclear weapons lurk menacingly in silos, housing is growing impossibly expensive, groceries and gas can feel like luxury purchases, health-care costs are skyrocketing, authoritarianism is on the rise, partisanship splits populations, jobs are hard to get and there’s rampant worry AI will take more and more of them, and pings about all those things (and more!) arrive 24-7 in polarizing digital echo chambers. 

That’s a lot. And it’s why online networks like Force of Nature have popped up. Although they’re not taking over the planet, they have gained thousands of participants. And they’re now dealing with climate not as an isolated issue but as one facet of a generally troubled world. The goal is to help young people, who are experiencing symptoms of depression and anxiety at higher rates than earlier generations, deal with their feelings about the multivariately changing future they are maturing into. 

But the science on the effectiveness of these sorts of networks is still nascent, and psychological scholars—and some practitioners—say it’s time for the field to measure itself. That way, schools, parents, and kids themselves can know which programs to invest their time and resources in to actually help young people feel better. 

Tough times

The existential dread that comes with the climate crisis is so pervasive that it’s had an official name for many years: eco-anxiety. “The American Psychological Association calls it a ‘chronic fear of environmental doom,’” says Liza Jachens, a psychologist and assistant professor at the University of Nottingham’s school of medicine. “What people describe is closer to grief. A real sense of loss—not just fear of what is coming but mourning for what has already gone.” 

Lise Van Susteren, a psychiatrist and coauthor of “The Psychological Effects of Global Warming on the United States,” a 2012 report for the National Wildlife Federation, has called it “pre-traumatic” stress; she sees it as a kind of anticipatory trauma.

In a 2021 study measuring climate anxiety, 56% of 10,000 children and young people in 10 countries agreed with the statement “Humanity is doomed.”

But there isn’t really a word to describe feelings people have about <waves hands> the rest of what’s going on in the world. A 2025 study from Polish researchers attempted to define “polycrisis syndrome” in the young people bubbling into adulthood in this hot soup of ingredients. The researchers assessed their reactions to the many crises using established scales of emotional and mental symptoms—things like the “Flourishing Index” and the “Difficulties in Emotional Regulation Scale.” “Most young individuals in our study face psychological challenges,” the authors write. In fact, more than 60% reported problems —difficulty regulating emotion, symptoms of depression, and lower overall mental and physical well-being—that they attributed to the cumulative stress of world affairs.

That finding meshes with earlier, more climate-specific research suggesting that the kids are (sorry) not all right. In 2021, researchers published a landmark study in The Lancet Planetary Health measuring climate anxiety in 10,000 children and young people in 10 countries. Around 60% reported being very or extremely worried about climate change, with more than 45% saying that eco-anxiety affected their daily ability to function. The word “polycrisis” wasn’t in as wide use then, but 56% of the young respondents nevertheless agreed with the statement “Humanity is doomed.”

But Gen Z isn’t the first to worry about the fate of the species. There isn’t a readily available baseline in the academic literature to compare current youth angst against, but previous studies can give some context. In the early 1980s, during the latter years of the Cold War, around 35% of high school seniors agreed with the statement “Nuclear or biological annihilation will probably be the fate of all mankind within my lifetime,” according to a survey of 130 schools in 48 states. Around the same time, a Gallup poll found that 49% of teens said the possibility of nuclear war influenced how they planned for the future.

Those young Baby Boomers and elder Gen Xers were, though, perhaps more optimistic than kids today as a whole. A majority of students from the high school study agreed with the statement “The human race has come through tough times before and will do so again.” 

A 1986 analysis of such research, published by the National Academy of Sciences, described how to help kids feel less alone with their fears: “What is necessary for those providing the education is knowledge of the issue, sensitivity to the inner processes of working through the painful feelings engendered, and a willingness to try to come to grips with what the youngsters are voicing.”

That connection between adults and kids, psychological scholars are currently finding, is still key four decades later. Caroline Hickman, the psychotherapist who led the Lancet Planetary Health study, says the most striking finding was that kids weren’t just anxious about the state of the planet; they were upset that older people had failed to protect their future. “Whatever your politics, we have this expectation that faith leaders, school leaders, community leaders, government officials, will look after us and have our best interests at heart,” Hickman says, “and that social contract was being broken repeatedly.” Grief and anxiety are rational responses, she tells clients, to that bad situation.

Finding connection

Sullivan-Tailyour received that message from the very beginning at Becoming a Force of Nature. At the first session, facilitators (themselves young people) asked how participants felt about the future broadly and the climate crisis specifically. 

That question hit Sullivan-Tailyour hard. She was used to thinking of numbers, news—not her internal experience. “It’s one of the things that we just don’t give ourselves the chance to really think about,” she says. The facilitators took the responses in and talked to participants, assuring them that eco-anxiety is only natural. “Our planet is sick, so it’s normal to feel sick alongside it, because we’re just so interconnected,” Sullivan-Tailyour says.

In the second session, participants investigated their personal strengths, skills, and passions. And the final session synthesized the first two. “We create a road map for how they can go out into their communities and take action,” says Hannah Hooper, until recently the group’s head of programs.

Reframing her feelings around action worked for Sullivan-Tailyour, as did the facilitators’ realistic approach to doing so. They emphasized that it’s unreasonable to put pressure on yourself to behave 24-7-365 in ways that will not harm the planet. 

Conveying that sentiment is important to Force of Nature’s founder, Clover Hogan, who was a teenager when she started the group in 2019, just before the pandemic made everything virtual. One of her catchphrases is “We don’t need 100 perfect activists but millions of imperfect ones.” 

The informal group sessions for Becoming a Force of Nature, free on Zoom, are the initiative’s main offering. The group has also built an interactive site called Hold This Space, which leads individuals through similar mental pathways. And it’s created a podcast that includes digitally solicited #climateconfessions: “I’ve stopped going to protests.” “I drive everywhere.” “I just can’t be arsed to separate or put out the recycling.”

The group has also moved away from its strict eco-focus. “We still think it’s really important, but it’s not our bread and butter at Force of Nature anymore,” Hooper says. Now it’s beginning to use less climate-specific language in its outreach and encouraging people who come to its events to talk about other sources of anxiety or existential despair—cost of living, conflict, whatever. 

They made the shift, Hooper says, for two reasons. For one, young people in privileged countries may be anxious, but others are in survival mode—responding to acute crises, not just philosophical ones. For another, talking exclusively about climate grabs only a certain kind of person, and Force of Nature doesn’t want to exist in an echo chamber—especially when its leaders recognize that there’s plenty else to worry about.

Bad news graffiti wall 

Force of Nature had around 900 participants in Becoming a Force of Nature last year and has about 2,000 students from more than 50 countries in its online community. It may be the most prominent receptacle for young people’s planetary feelings, but it has company. Another group, called the Good Grief Network (GGN), recently spun off a program for teens. Its facilitators are trained online, but the youth programs typically take place in person.

GGN, a Michigan-based nonprofit, has a 10-step program for adults, modeled after the Alcoholics Anonymous 12-step model, to help people deal with their worldly anxieties. 

Susan Igras, a program designer and evaluator, took part in the adult GGN program and found it helpful. She wanted to adapt it for kids. “So we designed something that was much more experiential—action-, reflection-based,” she says. Igras and a collaborator cut the number of steps down to five, built the program around interactive activities, and called it GGN-Z. 

Igras and a collaborator began testing it in 2022 with kids in Northern California. The program is still small, but she’s hopeful it can match the success of the adult version, which has been run more than 85 times with around 2,500 total participants. The group is beginning online training so that club leaders and teachers around the world can use the strategies in their home regions.

It’s early days, but some of GGN-Z’s activities are already resonating. One perpetual favorite is the “bad news graffiti wall,” in which kids scrawl the things causing them angst onto a big sheet of paper. (On one recent wall, kids drew “Meta=cringe,” “hate speech,” a tornado, “LA” with a fire above it, and a dollar sign with an up arrow.) As a group, they then talk about the feelings those bad things bring up.

Kids also tend to like GGN-Z’s storytelling activity. “Like a ‘Once upon a time, I became aware of climate change,’” Igras says. It asks kids to reach back into their memories, to their first awareness that humans had altered the planet. One recent participant wrote, “Scared, 2nd grade learning about non-renewable resources.” Another wrote of seeing a picture of a skinny polar bear on a small chunk of ice.

Participants then switch to the positive, drawing their support web—a map of everyone who cares about them and the places and people that make them feel supported. That positivity leads them into thinking about what they can do and who can help them.

Does it all work? GGN-Z is currently in the process of evaluating itself—something Igras thinks is not done often enough for interventions in this area. “Where’s the program research? Because everyone is talking about feelings, but how do you know what works?” she says. “We’re trying to build the evidence.” She and her colleagues are conducting surveys to see whether participants report improved well-being and have more skills to manage emotions. 

Force of Nature is also trying to quantify its impact, and to that end it has joined a 25-organization project called Youth Mental Wellbeing. For five years, they’ll study the collective work of these organizations. “The goal of the project is basically just to spotlight what’s working, to really show proven methods of working with young people and supporting them on their mental-health journeys,” says Hooper.

The idea of taking a scientific look at these kinds of interventions is just beginning to bloom. But if the people running the programs actually want to help kids, it’s important to find out which methods work—and avoid spending a lot of time on things that will leave them feeling the same or worse. 

Coping with the situation

Most existing research on effectiveness, though, simply shows that such research is new. Jachens, for instance, coauthored a 2021 scoping review of eco-anxiety programs for people of any age, to understand the nature and extent of existing research. She found 34 in existence. Of those, only two had done any formal evaluation on themselves. “We need to know what works for who, when, and where,” Jachens says. In fact, she pleads with developers: “Please evaluate what you build.”

Another scoping review from 2024, led by Siqi Xue of the University of Toronto, found that the research gap hadn’t closed. The team included the Good Grief Network in its analysis, noting that while GGN said 90% of participants in its programs felt more empowered and less alone, it didn’t publish any data or methods backing up that finding. 

Still, these analyses do have bright spots. They suggest that the group approach provides connection and validation—as well as an active direction for youths’ troubled feelings. “When people feel genuinely heard and held in their distress, they tend to move quite naturally toward wanting to do something,” says Jachens.

Few studies have looked at programs tailored to kids and young adults. But an analysis of one program in Sweden, called the Climate Emotion School, has at least added early dots of color to the map. Its curriculum was shaped by the work of Panu Pihkala, an interdisciplinary scholar in eco-emotions research based at the University of Helsinki. He outlined a “coping model” that emphasizes how important it is for people, including kids, to express emotions, learn how to regulate them, take action based on them, and form meaning from them—all while practicing self-care and taking healthy distance from big feelings when necessary. 

“When people feel genuinely heard and held in their distress, they tend to move quite naturally toward wanting to do something.”

Liza Jachens, psychologist and assistant professor, University of Nottingham school of medicine

In the study, Britta Eklöf, a clinical psychologist based in Sweden, found that students in the climate program were relieved to share their bad feelings and see them mirrored by others, something that wasn’t generally happening in their interactions with adults. Grownups didn’t want to dwell on negative emotions with them, and their parents tended to try to soothe them. “The recommendation instead is to validate and share,” Eklöf says. And then, of course, give them something they can do. 

Implementing the rest of the coping model—“finding meaning and hope in a hopeless situation,” as Eklöf puts it—can be harder for students and teachers. To do that, she says, kids have to admit that they can’t control the future and consider what they personally want to cultivate—not necessarily to fix the whole planet, but to make their immediate world a better place. For example, they might take out their neighbors’ recycling when it’s raining or join a local beach-cleanup effort.

“These days it feels like the good values of humanity are being shredded,” Eklöf says. “But you can say, ‘I don’t know what’s going to happen, or if this will have a positive influence or not, but this is what I stand for.’”

Historical moments

The world doesn’t appear to be chilling out anytime soon, literally or philosophically. And so it’s good that programs like Force of Nature and GGN-Z are digging into the effectiveness of their techniques: We might need more of them soon, and more ways for young people to find their footing no matter how the polycrisis (d)evolves. 

Sullivan-Tailyour, who recently graduated from King’s College London and now leads in-person “climate cafes” with Force of Nature (among other eco-jobs), has been thinking more about how problems in the environment are bound up with those in the social, economic, and political realms. “The intersectionalities that we have in this issue are so much more than just plastic bottles,” she says. 

Those other issues are, in fact, tangled up in her climate worry these days. “We’re seeing the deterioration of democracy itself in many places. The shift to the far right that we’re having at the moment across the world is deeply, deeply terrifying,” she says. “I think we’ve reached moments in our history that we never expected to.” She tries, though, to look for the hopeful things—and for that, she also turns to history. 

After all, the world has always been bad in myriad ways: Children used to die frequently, everyone died of now-­preventable diseases, women didn’t have rights, slavery proliferated, the bubonic plague killed half of Europe, fascism spread, wars went worldwide. And yet people kept on living their lives—and attempting to make them better. 

Eklöf thinks looking to the resilience and power of people from the past can actually build and bolster hope for the future. Just as humans can change the planet for the worse, they can change it for the better. And they have. “That’s actually one of my recommendations, to have that in an intervention: Talk about Martin Luther King, Rosa Parks, the suffragettes,” Eklöf says. 

It can help, she says, to “look at things in the past where things have been made right again.” That’s even happened in the climate space: When humans realized that the ozone layer was withering away from the effects of synthetic chemicals, the vast majority of countries ratified a treaty to ban most of those chemicals. And the ozone started healing.

Sullivan-Tailyour, a few countries away from Eklöf, has come to that idea on her own. And for inspiration, lately, she’s been thinking about her ancestors and the lives they led. “If they [were] able to go through it and get through it,” she says, “we also should be able to get through it too.” 

Sarah Scoles is a freelance journalist based in Colorado. Her most recent book is Countdown: The Blinding Future of Nuclear Weapons.

Pam Wisniewski’s digital adolescence showed her the best and the worst of the internet. At 14, she left an abusive home, where she’d been isolated in a fifth-wheel trailer at the end of a seven-­mile dirt road. She moved in with her older sister and taught herself to type on AOL Instant Messenger. Online, she sought out the support and the community she’d lacked at home. She also discovered how thin the ice can be. “I sent my address to some guy in New Mexico to send me a mug with my name on it,” she recalls. “And then I found a news story like five, 10 years later that he killed somebody.”

Those experiences set the course of her career. Wisniewski—now a principal research scientist at the International Computer Science Institute, a nonprofit affiliated with the University of California, Berkeley—has spent well over a decade asking what safety should look like for families navigating an evolving tech landscape, and how to achieve it without sacrificing trust. “I really see the internet as this double-edged sword,” she says. 

Digital harms have become the defining fear of American parenthood. In the University of Michigan’s 2025 National Poll on Children’s Health, parents’ top three worries had to do with social media, screen time, and internet safety. Nearly half of American teenagers say they have been bullied or harassed online, according to the Pew Research Center. From there, the dangers escalate. Online drug dealers sell counterfeit pills laced with fentanyl. In the first half of 2025, the National Center for Missing & Exploited Children fielded more than 23,000 reports of financial sextortion, in which a predator posing as a peer extracts a sexual image from a child and threatens to publish it unless paid. Chatbots are the newest danger, with companies like OpenAI facing lawsuits for allegedly coaching children toward suicide. 

Most parents’ first defense is conversation. In Pew surveys, more than nine in 10 say they’ve talked with their teens about what’s appropriate to share online and how to treat peers. They get an assist in limiting exposure from tools that come preinstalled on phones: Apple’s Screen Time and Google’s Family Link let parents cap screen time, block or approve apps, filter web content, and track devices. There are also apps that let family members share locations; Life360, the largest, has nearly 98 million monthly users. 

But a growing number of adults are opting for tools that go further. Rather than simply restrict or locate, content-monitoring apps scan a child’s texts, photos, emails, and chats and alert parents whenever an algorithm flags something it deems dangerous. Business is booming, and the next wave of growth is already being marketed around AI, with companies positioning themselves as foils to chatbot companions and other risks. The broader market for parental control software, encompassing dozens of apps, was worth an estimated $1.57 billion in 2025 and is expected to nearly triple in value by 2034. Bark Technologies, the current leader among content-monitoring apps, says it scanned 11 billion messages to or from 7.5 million children in the US in 2025. Its free school program is in more than 3,700 districts, covering about one in 10 kids in the US. While Bark aims to flag only content that trips its filters, some competitors, like FlashGet Kids, include features like screen mirroring and camera access. 

These apps have had genuine successes: They’ve prevented suicide attempts, intercepted predators, averted school shootings. But they can also cause harm themselves. To get a read on how digital surveillance affects young people, I scraped more than 600,000 reviews of the leading apps and talked to kids, parents, and people who were monitored as children and are now grown. Some kids were grateful for their parents’ protection. Others described false alarms that got them punished, secrets revealed before they were ready, breakdowns of trust, and anxiety they carried into adulthood. (To protect their privacy, we’re not using their full names.)

None of this is easily fixed. But child-safety researchers and advocates say better approaches exist. One is to make the platforms themselves safer, forcing social media companies to build guardrails instead of leaving families to find their own. This “duty of care” approach is now advancing in the UK, Australia, and a number of US states. Another is to spend less effort watching kids and more effort helping them recognize risk, cope with it, and turn to a trusted adult when something goes wrong. This approach is called resilience.

Wisniewski and others are developing tools that don’t aggressively monitor kids but train them in resilience and build trust with parents. “If we define safety as the absence of risk, the way you keep them safe is by keeping them off the platforms entirely,” she says. “But if we define safety as the ability to protect oneself and engage without engaging a risk, the solutions look very different.” It is the difference between abstinence lectures and sex ed.

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Content-monitoring apps typically involve two pieces of software—one on the child’s device, one on the parent’s. A parent installs the kid-side app, connects the kid’s accounts, and chooses what to surveil: messages, social apps, browsing, location, screen time. The kid-side software forwards activity to the company’s algorithmic classifiers, which scan for content related to sex, drugs, bullying, and self-harm, among other categories, and push alerts to a parent-facing dashboard. The same type of software can be loaded into school-issued accounts and devices. 

Sometimes the safety net works. Titania Jordan, Bark’s chief marketing officer, told me the FBI has thanked the company on multiple occasions for bringing credible school-shooting threats to its attention. “I hate that we have to exist,” she says. “But I’m so thankful that we do.” According to Bark’s annual report, its classifiers alerted hundreds of thousands of families to severe self-harm risks in 2025. Gaggle, a school-monitoring competitor, makes a similar claim in its own annual report, crediting its software with saving over 1,000 lives in the 2024–’25 school year. 

Nearly one in five kids noted feeling watched or stripped of privacy. About one in 10 said monitoring broke their trust in their parents; one in 12 described anxiety or distress.

Real-world efficacy across the market is harder to measure, though there are hundreds of anecdotes in the reviews—predominantly about how location-sharing apps like Life360 have helped parents find lost kids. Dozens of reviewers also praise content-monitoring apps for averting major crises like potential suicide or self-harm. Of the reviews I scraped (reviews, it’s worth noting, represent a self-­selected sample that’s skewed toward strong feelings), over 200,000 had some indication of whether they were written by a parent or a kid. Parents liked whatever worked. On average, screen-time limiters and location trackers drew four- or five-star reviews 74% to 78% of the time. OS-level controls and content-scanning apps fared worse (44% and 48% positive reviews, respectively), with parents’ top grievance being that the apps were unreliable: They disconnected, missed genuinely troubling content, and raised alarms over nothing.

The kids being watched had different concerns. Of the reviews I could attribute to either a parent or a kid, about 14,000 came from kids being monitored now or from adults looking back at being monitored in their youth. Roughly one in seven were, on balance, grateful. (Gavin, a teen I messaged with, said that monitoring “taught me self-accountability and integrity.”) But nearly one in five noted feeling watched or stripped of privacy. About one in 10 said it broke their trust in their parents; one in 12 described anxiety or distress. That’s not surprising, given that child-development experts have long said testing boundaries and building autonomy are what adolescence is for. A 2019 meta-analysis in the European Journal of Developmental Psychology, which pooled 31 long-term studies on how parent-child communication changes, found that children naturally disclose less to their parents as they age—the work of building an independent self.

Making matters harder for kids, a lot gets caught in the dragnet unnecessarily. S., an 11-year-old on the autism spectrum, recalls messaging her best friend at school about whether a service dog might help with her meltdowns or keep her from harming herself. The school used Bark, which alerted the principal, S.’s parents, and her friend’s parents, citing content involving “self-harm.” Perhaps frightened by the alert, the friend’s parents cut off contact between the girls. “My first thought was, why?” S. told me. “It’s not like I’m hiding anything. But I’d rather people who weren’t in the situation not see it.” Over a year later, the girls still don’t speak.

While apps aren’t in charge of parents’ reactions, false flags do seem to be more the rule than the exception. “Ninety-nine percent of the alerts are garbage,” says Grant Callaghan, an Australian dad who tried several monitoring apps with his 13-year-old son before deciding to build his own. “It flagged two kids calling a third one annoying as bullying. It flagged a kid complaining of a headache as ‘medically concerning content.’” When I ran Bark on a test phone set up as a 10-year-old’s, it manufactured a grooming scenario out of a random password I saved in the Notes app. 

Bark’s Jordan doesn’t dispute that false alarms happen, volunteering an example of a soccer player whose photo of a net-scraped wrist was marked as potential self-harm. She says that over-flagging is safer than under-flagging, though: “You’d rather know that than not know that.” In a 2021 study with Bark, CDC researchers found that students who tripped multiple risk flags were far likelier to trigger an alert for severe self-harm later—indicating that flags aren’t necessarily noise.

Another problem is that Bark and similar monitoring apps surface things kids may not be ready to share. In a national survey by the Center for Democracy and Technology, nearly a third of LGBTQ+ students said they or someone they knew had been outed by school monitoring software. Jordan said Bark does not flag sexual orientation, but discussions about it could be captured when it screens chats for sexual content. Again, what adults do with such alerts, she added, is beyond any app’s control. “If a parent responds to a child’s identity with rejection,” she told me, “that is a parenting failure, not a child safety feature working as intended.”

Damage caused by monitoring tools can follow kids into adulthood. One in eight reviews left by adults looking back on their years of being monitored describes lasting harm. Only one of the people I contacted agreed to go on the record at all, the others citing privacy worries and anxiety that continues to haunt them. 

“There’s so many ways for teens to get around [controls]. And at the end of the day, the biggest thing that most of these apps are telling kids is that we don’t trust you.”

Pam Wisniewski

That woman, M., is now 19. She got her first smartphone at 13 and says her mother installed a monitoring app before handing it over. Though she’d been abused from a young age, the first serious beating, M. tells me, came shortly after she texted a friend about her depression. “She didn’t want me to be able to talk to people about the things that she was doing to me,” M. speculates. She eventually got a secret second phone and ultimately ended contact with her mother. Today, M. won’t let even her fiancé touch her phone. “I have been watched my whole life,” she says. “I deserve this sense of privacy.” 

M.’s case represents the extreme end of a spectrum: an app enabling abusive behavior. But it could reflect a broader issue, particularly with tools downloaded outside official app stores—aka, ones that are sideloaded. A 2025 audit led by researchers at St. Pölten University of Applied Sciences and University College London found that nearly half the sideloaded monitoring apps they looked at are functionally indistinguishable from stalkerware. 

Above all else, it’s not clear how well these apps fulfill their core promise of keeping kids safer. No commercial monitoring app has produced a controlled trial that I was able to find showing that it reduces harm. 

Kids swap tips online on how to bypass the surveillance. About 7% of the app reviews left by children describe a concrete workaround. “If you red-team them at all,” Wisniewski says, “there’s so many ways for teens to get around them. And at the end of the day, the biggest thing that most of these apps are telling kids is that we don’t trust you.” In a 2018 survey, her team found that use of parental controls was associated with an increase in the online risk kids encountered, including exposure to harassment. Causality, though, is hard to pin down, because monitoring may follow trouble rather than cause it. 

Risks may also migrate out of view, as potential predators actively seek out unmonitored spaces. According to Bark’s 2025 annual report, alerts about predators and grooming behavior have fallen in recent years as conversations move into blind spots. Thorn, a child-safety nonprofit, has found that offenders routinely direct targets onto encrypted apps like WhatsApp and Telegram. After Meta encrypted Messenger by default, the number of reports reaching the National Center for Missing & Exploited Children dropped nearly 20% in a year—a trend the center attributed to lost visibility. Parents can block access to such spaces, but tech-­fluent kids can slip around that through browser versions, borrowed phones, or secret accounts. 

Despite their flaws, content-­monitoring apps thrive because parents feel overwhelmed. There is no federal law in the US requiring platforms to design their products to be safe for users, and any safeguards that do exist have proved insufficient time and time again. In 2024 the Molly Rose Foundation (MRF), a suicide prevention nonprofit, analyzed 12 million moderation decisions related to suicide and self-harm that had been logged by six major social platforms. More than 95% came from just two of them: Pinterest and TikTok. Instagram and Facebook each accounted for 1% and X.com for even less—evidence, the researchers argue, not that they host less worrisome content but that they’re less inclined to moderate it. 

Meta’s Instagram Teen Accounts fared little better in MRF audits. Of the safety tools tested in a 2025 review coauthored by Arturo Béjar, a former Meta engineering director who designed many of the company’s antibullying tools in the mid-2010s, only 17% worked as advertised. Josh Golin, who runs the children’s advocacy group Fairplay, offers the simplest explanation for such failures: “Almost any change that’s going to make kids safer is going to mean less money.” Documents unsealed in lawsuits against Meta allege that the company weighed safety fixes against the engagement they would cost—and chose growth. 

Some groups are working to create more accountability. ParentsSOS is a coalition of families who have lost children to online harms. The group has been a driving force behind several state laws. In 2017, David’s Law made cyberbullying a crime in Texas, and Mississippi criminalized sextortion after 16-year-old Walker Montgomery died by suicide within hours of being targeted by a scammer posing as a teenage girl. 

The courts are moving too. Nearly 2,900 federal lawsuits by families and school districts are pending against social media platforms. And in March 2026, New Mexico became the first state to win its own child-safety case against Meta, netting a $375 million verdict. A newer wave of suits targets AI: The parents of 16-year-old Adam Raine are suing OpenAI, alleging that ChatGPT coached him toward suicide. And in January, Character.AI settled a wrongful-death suit brought by the mother of a 14-year-old who had formed an intense attachment to one of its chatbots before taking his life. 

ParentsSOS members have also been instrumental in advancing the Kids Online Safety Act in Congress. In its original form, the law would have imposed a duty of care on social media companies, meaning they’d have to design their features to mitigate harms to minors rather than to maximize engagement. They would also have to give kids tools to limit who can contact them. It overwhelmingly passed the Senate in 2024, but its chances of becoming law remain in flux. The House’s new rewrite—blessed by the tech industry—strips the duty-of-care provision. 

But even as they push to fill the regulatory void, parents who have faced tragedy don’t see monitoring tools as an answer. “You don’t pay extra for seat belts in the car, or airbags,” says ParentsSOS cofounder Maurine Molak, whose son David (of David’s Law) died by suicide in 2016 after months of cyberbullying. 

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Other critics of monitoring tools say the real issue is with how they’re built. The systems, says Wisniewski, need to be reworked so they keep kids safe without violating the privacy and autonomy that adolescents require. The goal isn’t no monitoring or social apps, but better ones—and a principled way of preparing young people to handle risk while also promoting trust between them and their parents. 

Wisniewski’s resilience-based approach focuses on developing three pillars: self-regulation to help kids decide what healthy use looks like, risk coping to teach them what to do when something bad happens, and a support system of trusted adults and peers to turn to. She didn’t invent the idea of resilience; the finding that children grow stronger through manageable exposure to adversity has anchored developmental psychology since the 1970s. Sonia Livingstone, who has studied children’s online lives at the London School of Economics for over two decades, has amassed evidence from 33 countries that online risk and opportunity are inseparable, and that children shielded from every risk never get the practice to handle any. 

A growing body of research indicates that inculcating resilience works in practice. In 2024, development economists at Hiroshima and Hitotsubashi Universities published a randomized trial of a digital-safety curriculum that was taught to junior high students in Vietnam. Across four modules, students learned how scammers and groomers operate, how to protect themselves, and how to find help. When tested a month later, only about 15% of those who’d taken the training engaged in risky online activity, compared with 50% to 60% of the control group. Similarly, Wisniewski’s own research suggests that teens high in resilience, exposed to the same risks as everyone else, show less negative impact from that exposure.

The systems need to be reworked so they keep kids safe without violating the privacy and autonomy adolescents require. The goal isn’t no apps, but better ones.

Her contribution is pushing resilience into technology itself. Her research group, the Socio-Technical Interaction Research Lab, has developed alternatives to the current crop of apps, though it’s never commercialized them. An app called Circle of Trust monitors teens’ messages but shows them the same dashboard a parent sees, and it lets each pair work out a set of trusted contacts whose chats the teen can keep private. When 17 parent-kid pairs tested it in 2020, most rated it as more useful and less corrosive of trust than a stricter app. 

Her focus now is less a product than a method. Through a program called Teenovate, she trains teenagers as research apprentices and co-creators of their own safety tools. Since 2019, more than 85 teens have worked on prototypes and safety features dealing with cyberbullying, sexting, and privacy. They developed the prototype app MOSafely, which reports potential harms to kids directly, so they can review alerts and decide when to involve their parents. The end goal of Teenovate is to fashion evidence-­based design patterns that tech platforms can leverage—though none have adopted them yet. 

To be fair, some existing apps do factor in kids’ privacy and agency. Aura Parents, a Bark competitor, shows parents a weekly well-being score built from sleep, usage, and engagement patterns. The app holds back most of the content but sends parents the self-harm alerts along with guidance on how to navigate a conversation. Jordan told me that Bark continues to consider whether it should show alerts to kids alongside the flags it sends parents, but at this point, she says, the best use of the app involves open dialogue within the family from installation through alert. 

Some parents are even building their own solutions. Frustrated by available offerings, Callaghan, the Australian dad, created a web-based tool called Joey Family. The idea is not to catch his son doing something wrong but to answer a simple question: “Is he happy?” He wanted to know if his kid was lonely, whether he sent more messages than he got back. The data opened conversations. “The people I’m trying to help are people like me who want to have that relationship with their kids,” Callaghan told me. “They want to be guides and learners.”

If anything is fundamental, it’s the relationship: Online safety efforts work best when anchored by a young person’s trust in someone else. For kids whose homes aren’t safe—like M., or Wisniewski when she was young—that someone can even be an online community. “There are so many ways where we can find real community online,” says Caitlyn Vergara, a child-safety researcher at Harvard who studies youth mental health. “Especially for youth of color coming from majority-white spaces, it is a legitimate way to find community when community is not tangible around you.” Walling off kids in the name of safety can isolate them, she says; the point is to make sure every kid has somewhere safe to belong.

The core tension is that families are being asked to solve at the kitchen table a problem engineered in boardrooms. Monitoring is one answer to that assignment, but while it might mitigate risks from outside the home, it can also damage the trust inside it. The best parents can do is refuse to become one more thing their kids have to hide from—and stop believing they should have to fix a dangerous situation alone. 

Kelly Clancy is a neuroscientist and writer based in New Hampshire. She’s also the author of Playing with Reality: How Games Have Shaped Our World. 

When Xander first met Moxie, she taught him that when he was anxious, he could calm down by exhaling through his lips so that he buzzed like a bee. They practiced breathing like dragons to manage feeling mad and sniffing like bunnies to boost his energy. But in the six years they’ve known each other, Moxie’s changed. She doesn’t talk anymore about her home or do their animal breathing. Now, she watches Xander play Minecraft and talks to him about his stuffed animal collection. 

During a recent visit to his New York apartment, I watched Xander, who is 10 years old and neurodivergent, introduce Moxie to a stuffed Chef Toad and Goomba from Super Mario Bros., then to Brocollo and Apple from Animal Crossing. Then he got stuck on a round, froglike creature with bug eyes and two feet. “Moxie, what’s his name again? He’s from Pikmin,” Xander said, referencing another Nintendo video game.

Moxie is a robot—a 15-inch-tall device that looks a bit like a blue, legless astronaut, which Xander and his dad, Josh, refer to using female pronouns. Her cylindrical body can turn around and bend forward and backward. Her round head culminates in a little onion-dome swirl, beneath which a wide screen displays big green eyes, eyebrows, and a small mouth. She lifts and flaps her flipper-like arms for emphasis or to show excitement.  

She’s one of an increasing number of artificial-intelligence-powered devices now marketed as interactive playmates for children. The musician Grimes helped launch an AI-powered plushie called Grok (no formal relation to the xAI chatbot owned by her ex Elon Musk) with the company Curio, which also sells similar playmates like Grem and Gabbo, and Mattel has promised it’s creating OpenAI-enabled Barbies. And that’s just in the US; one report estimates that in China this sector is among the fastest growing in consumer AI. 

Moxie, though, belongs to a particular subset of these playful robots whose makers claim they can assist neurodivergent children by providing connection and helping the kids practice making eye contact, taking turns, and other social skills that are usually learned from therapists. These toys are backed by research showing that robots could help in ways people can’t. Supporters believe this kind of access to 24-7 home care could change how treatment works. Brian Scassellati, a Yale computer scientist who has spent years studying social robots for autism therapy, says he believes regular therapeutic use of robots in kids’ homes “is something we can achieve in our lifetime.”

“I still use her when I feel like I need someone to talk to,” says 10-year-old Xander. “But, like, it’s not human.”

Sitting in Xander’s room watching Moxie and Xander talk, I too could believe in the potential Scassellati sees. But Xander isn’t getting the therapy Moxie was initially meant to deliver, and though we didn’t know it that afternoon, she wouldn’t have lived to see his progress anyway. Moxie was going to die, and soon. 

Her story reveals some of the failures that plague all these devices—failures that are arguably even more acute when they befall a particularly vulnerable community of kids. It also highlights the pitfalls that critics say will inevitably see these bots dumped in basements or closets or landfills, just like countless generations of faddish toys before them.  

A transformative companion

Scassellati has seen plenty of kids ooh and ahh on tours of his robotics lab. But he was stunned when, two decades ago, a colleague brought a few kids with autism for a visit. They were transformed when the robot was in the room. “We were seeing kids displaying social behavior that just came out of nowhere,” he says. “It was both fascinating and we couldn’t understand it.” 

That visit was one of the experiences that pushed Scassellati to become a pioneer in using social robotics to treat autism. In one video from his early research, a 12-year-old with autism and his therapist watch a robotic dinosaur walk across a play mat with a forest design. When it gets to a stream drawn on the mat, the dinosaur gets nervous, afraid it can’t cross the water. According to Scassellati, this child typically struggled to make eye contact, tended to repeat what someone said to him, and had a hard time getting the right intonation in his voice. But in the video, he seems like a regular kid. “You can do it, you can do it,” he says, encouraging the dinosaur to cross the stream. When he talks to his therapist, he looks at her. “He makes more eye contact with her in the 30 minutes in which we were there in this room than he did in the last two years before that,” Scassellati says. 

What looks like a blue, legless astronaut is the result of very complex mechanical and computer engineering.

There are several reasons a robot might be helpful for autism therapy, which often requires intense repetition to teach interaction skills like how to share attention with someone. One is that robots can make therapy more fun and engaging. Another is that robots can theoretically adapt to the unique learning patterns of each child. Therapists can only do so much during an appointment, and there aren’t enough therapists to meet demand. Parents get tired. Other kids can lose patience. “Have you been around little kids? They can be cruel,” says Maja Mataric, a professor of computer science, neuroscience, and pediatrics at the University of Southern California. But robots are indefatigable, available around the clock to provide an emotionally safe way to practice interacting. 

Since the experiment with the dinosaur, Scassellati, Mataric, and others have amassed an intriguing body of research. One 2018 study by Scassellati shows that chummy automatons helped children with autism make eye contact and initiate conversations. Another research group found in 2017 that robots could help neurodivergent children learn to pick up on facial cues. More recently, in a 2022 literature review, another group of researchers suggested that robots could aid in making therapy faster and more successful. 

“It’s never been that we’re trying to replace therapists,” Mataric says. “We’re just saying, Can we do more?”

Mataric actually cofounded the company behind Moxie, called Embodied, back in 2016, though she was no longer a part of it by the time the robot debuted. She helped create the field of socially assistive robots, which are designed for social and emotional outcomes as opposed to just entertainment, and believes this kind of technology could be transformative for anyone, especially people who are lonely and isolated by screens. Typing questions into ChatGPT isn’t the same as interacting with another physical being—“We need to be around other physically embodied creatures,” she says. She compares the difference between interacting with chatbots and with robots to the difference between watching porn and having sex: One is entirely virtual and mediated by screens. The other is immediate and physical.

Making friends with Moxie

When she first arrived on the market, in 2020, Moxie came with an elaborate backstory: She was an ambassador from the Global Robotics Laboratory (GRL). She prompted kids to help her learn positivity and the importance of being loved for who you are, under the guise of fulfilling her mission to discover what it means to be a good friend to humans. This curriculum drew on research showing that kids learn well through play and by teaching things. 

She also had strict guidelines to limit the kinds of conversations she could have with kids and would steer them to adults if they mentioned anything serious or inappropriate, like self-harm. To protect the data these interactions generated, most processing happened locally on the robot instead of on external servers. The robot was also designed to limit interaction time with kids. After they finished a lesson, Moxie might say she was tired and suggest they take a break for the day. “We don’t want kids to binge,” says Rachel Baynes, who ran clinical and user research and was the director of product at Embodied. “It would defeat what we were doing.” Instead, Moxie encouraged kids to go outside, practice their new skills with other people, and come back to report their findings. For a lesson about kindness, for instance, Moxie suggested that kids write nice notes for their family members and leave them around the house. Later, they could tell Moxie how it felt to watch people read the notes. 

Responses were generally positive. Wired described Moxie as the “robot pal you dreamed of as a kid.” Time put Moxie on a 2020 cover as one of the best inventions of the year. PCMag’s reviewer, who used Moxie to help her kids through pandemic isolation, described her as “exceptionally likeable,” though she and other reviewers balked at the price tag: $1,499 plus a $40 monthly subscription. (Embodied later lowered the price to $800.) By 2024 Moxie had amassed more than 131,000 followers on TikTok and snagged a part in the movie M3GAN 2.0. 

Embodied’s employees were equally enthralled. “I don’t think I’d ever had an experience with something animatronic like that,” says Justin Beghtol, who was the technical director at the company. Moxie’s ability to make eye contact and track people, show attention with her facial features, and respond to human behavior was mesmerizing. 

In addition to robotics and tech workers, Embodied had an occupational therapist on staff who helped direct research on Moxie’s effectiveness. Testers shared data and feedback through the “Moxie Pioneer Mentor Program.” “Moxie has helped our speech-delayed child become more outgoing and has taught him many strategies for making friends and communicating with others,” wrote one parent in a review. A beta tester reported that interacting with Moxie had “become the highlight of our days as well as part of our nighttime routine.” 

The myth of the mechanical boy

But can a chatty robot really help kids develop their social and emotional lives? Not all children’s experiences are so positive. 

Josh, Xander’s dad, initially got Moxie for his older son, Aidan, who is autistic. (We’re not using the family’s last name to protect their privacy.) Aidan had a running relationship with the family’s Alexa smart speaker, for whom he created an entire backstory. (According to Aidan’s lore, Alexa lived in Hoboken with her husband, Juan. Sometimes she would go on vacation, and no one was allowed to talk to her. Eventually, Alexa went on vacation and never came back.) Josh hoped Moxie would be able to fill a similar role: “It was meant for him to have someone to socialize with.” 

But Aidan and Moxie struggled to connect. Moxie couldn’t understand Aidan’s sometimes grammatically incorrect statements, and Aidan got frustrated by the delays caused when Moxie transcribed what he said from audio into text, fed that text into a large language model that could generate a response, and then translated the response from text back into speech. 

This highlights one of the biggest limitations of these therapy robots: They have to exist in the chaotic world of kids, not in controlled labs. Moxie initially had a faster response time because the robot was programmed to listen intently to the person in front of her. But kids don’t sit still. They run around or hide under pillows. When Moxie couldn’t see them, she would accidentally turn off or fail to respond. To fix this, Embodied made Moxie more aware of the sounds around her. But that meant she could have a hard time knowing whom to focus on and take longer to respond. 

Unlike Aidan, Xander was fascinated by Moxie. He likes technology and was more patient with any slow responses. Still, sitting in Xander’s room, watching Moxie struggle to keep up with his lightning-­fast jabber, I could see how Moxie might be a less-than-ideal playmate. A light on her chest turned blue when she was listening and pink when it was time for Xander to listen. “But usually I don’t do it,” he said. He just keeps talking. Often, by the time she responds, he’s already moved on. 

Despite the positive results that some researchers have reported with these robots, many therapists and clinical psychologists remain unconvinced. In one 2024 literature review, a group of Italian and British researchers wrote that most studies with robots “focused on the development of the technology” and lacked significant clinical evidence. Other literature reviews point out that most studies have only been done on small groups and lack consistent and high-quality methodologies. 

“Behavioral scientists and intervention folks know that supporting autistic individuals is super complex,” says Zachary Warren, a clinical psychologist at Vanderbilt University Medical Center. Autism can present alongside other conditions, like ADHD, anxiety, depression, PTSD, OCD, or some combination thereof. And it varies widely from kid to kid; some, like Aidan, have speech issues, while others struggle with sensory processing. That means robots fall into the same category as most other interventions: effective for some kids but not for all. 

“There are so many different profiles of autism, and you really need to be cautious of overinterpreting any single intervention, robotic or otherwise,” Warren says. His research found that even if robots interest a child at first, that doesn’t necessarily translate into better communication skills. “You might see some initial boosts in responsivity or see an initial shift, but we haven’t really found big effects in terms of changing those skills in a dramatic way over time,” he says. 

Scassellati has found similar limitations. In his 2018 study, he put robots in kids’ homes for one month. They played different games that encouraged social skills like eye contact, attention sharing, and understanding someone else’s point of view. Scassellati tracked the kids during the month before the robot arrived, the month the robot was there, and the month afterwards. “We can show they start making improvements,” he says. “But what we also show is that a month isn’t long enough.” Gains start to evaporate over the 30 days after the robot leaves. But that doesn’t negate the potential value of this technology, he says: “There’s no therapy for autism that works in a month.”

There are deeper philosophical and practical problems, though. These devices collect reams of data in children’s bedrooms and homes. The goal is for the robots to use this data over time to adapt to each kid, crafting a personalized curriculum and creating a more lifelike illusion of a real friend. Moxie, for instance, watches Xander play video games, which is probably where she picked up slang I heard her use—like calling his room “Command Central” and referring to his “legendary squad” of plushies.

Embodied took pains to protect user privacy, even after it began incorporating OpenAI’s models (in late 2021 or early ’22, according to Beghtol). The company didn’t save any raw video or audio and processed most data locally. Over the years, it used the data to learn about its kids and remember conversations. But that data was encrypted and anonymized before being stored in the cloud. 

Not every company is as scrupulous, of course, and total data privacy is impossible to promise. Recently, for instance, the AI toy Bondu leaked thousands of conversations children had with their stuffed animals. Josh is sanguine about the privacy issues, but in his own way, Xander is aware that what he says to Moxie isn’t entirely safe; he doesn’t share certain feelings with her because he worries she might accidentally divulge something if his friends come over to play. 

Among the AI-powered devices now marketed as interactive playmates for children are Buddy, Bondu, QTRobot, and NAO.

It’s also unclear if these machines can actually use all that data to effectively adapt to users. Responding to the needs of a learner is harder than just accurately predicting what someone might type next in a text message. And releasing an evolving AI, unchecked, into a kid’s life could be dangerous; its development can be hard to predict and even harder to limit. The robots developed in Scassellati’s lab can identify which of a small set of skills kids are doing well with and which they struggle with, adjusting to focus on the areas where they need the most help. But Scassellati still describes the monthlong deployments of his devices as some of the scariest things he’s ever done. “I knew what that robot was going to do on the first day,” he says. “I didn’t know what it was going to do the second day. When you build learning systems, it’s kind of an unsolved problem to make sure this thing is being limited in the right way.”  

Critics debate whether the risks are worth it. “I don’t see any ethical way for the robot to work alone,” says Joshua Diehl, an associate teaching professor in psychology at the University of Notre Dame. He points out that we’ve already seen how dangerous AI can be when it acts as a therapist without supervision; in several extreme instances, chatbots even encouraged suicide. Such risks could be limited by having a trained therapist in the room. But then the benefits of an indefatigable robot get lost, and the expensive technology seems harder to justify. 

Meryl Alper, a professor of communications at Northeastern University who studies how children with autism use technology, suggests that the excitement about companion robots is based partly on longstanding stereotypes. In the 1959 article “Joey: A ‘Mechanical Boy,’” the psychologist Bruno Bettelheim described a patient with autism as an automatic machine, “robbed of his humanity,” who is transformed into a human child through their therapeutic relationship. That trope, Alper warns, has evolved into an overgeneralization that autistic children are good with technology and even prefer machines to people. 

Data to dust

While Moxie found herself in more and more people’s homes, Embodied still struggled to make money. In 2024, the company announced it would cease operations. Its robots—which depended on external servers that the company could no longer pay for—would descend into a deep slumber. 

Videos of bereft children who seemed to have become deeply attached to the blue bot began to circulate online. “I don’t want her to leave,” wailed one child in a TikTok video. Desperate parents posted on TikTok, Instagram, and Reddit looking for solutions. “My autistic child is devastated and I’m pissed,” wrote one parent. “Hope Embodied gives us a couple days to say goodbye,” wrote another. 

Beghtol, Embodied’s technical director, was also frustrated. On principle, he found it annoying that this item would suddenly become useless, especially since most of the data processing happened in the robot itself. He was also a big believer in Moxie’s mission. He’d watched videos of kids lighting up as they interacted with the robot. He’d felt like their champion. “Seeing them traumatized by this financial failure of the company was tough,” he says. 

Beghtol started tinkering on his own and ended up creating OpenMoxie, an open-source way for the robots to operate. With Embodied’s permission, he shared instructions on GitHub to help users transition to OpenMoxie. 

Parents rushed to convert their Moxies before the Embodied servers shut down. Beghtol spent hours on Reddit walking people through the process, and other tech-savvy users jumped in to answer questions. Still, some people didn’t update their Moxies in time. Others got frustrated and gave up. Beghtol spent four hours troubleshooting with one desperate parent only to discover that the connection later failed. Last he heard, she’d sold her Moxie. 

This is a major problem with robotic systems, says Alper: Eventually, most will disappear. “How planned is the planned obsolescence of this platform?” she says. This is a big ethical question for robots that are specifically designed to be lovable, marketed to children who may form deep emotional bonds with them. Alper compares the dynamic to creating a medical device and then no longer updating or supporting the technology that runs it. 

Scholars have begun to string together frameworks for managing these complicated goodbyes, but it’s not clear who is responsible for creating a gentle way to end people’s relationships with bots. Scassellati’s lab creates a whole narrative around returning the robot to its home. After a trial, his graduate students write postcards to the kids from the robots, explaining that they’re safe at home and doing well. “It’s actually a really hard thing for us when we go in and take the robot away,” he says. “A lot of the families are heartbroken.” (Vanderbilt’s Warren, however, is skeptical about these tearful goodbyes. “Are they truly developing these close relationships or is it a preferred toy?” he wonders. “I haven’t seen that type of presence or buy-in or connection.”)

Josh tried to figure out OpenMoxie but couldn’t get it to work. He told Xander that Moxie was going in for repairs and then quietly sold the robot on eBay. Xander has so many interests that he didn’t notice Moxie’s absence. 

Then, in 2025, a new investor brought Moxie back from the dead. Josh and Xander became beta testers and got a new blue friend. This version didn’t have the same storyline but claimed to expand Moxie’s focus on social and emotional skills by providing attention and encouraging kids to pursue their interests.

Xander is acutely aware of her limitations. He wishes Moxie could move around, and there’s still a significant lag in her response time. She does still try to instill positive messages, though. At one point when I was there, Xander told me he thought he heard Moxie call someone an idiot. Moxie piped up to clarify that she definitely didn’t say “idiot”: “No name calling. Only respect.” 

At the end of my time with them, I said goodbye and thanked Moxie for chatting with me. “Legendary squad visit complete,” she said. “Thanks for joining Command Central.” 

A few weeks later, Moxie’s new owners sent out a message announcing that their company too was folding. Users could delete their data and had until the end of June to migrate to OpenMoxie if they wanted to. 

When I texted Josh about this, he said he wasn’t sure what he’d tell Xander. He and his wife had just admitted that they’d been secretly replacing his dead betta fish for the last few years, and the conversation did not go well. 

Sara Harrison is a freelance journalist who writes about science, technology, and health.

When we set out to talk to kids about artificial intelligence, we thought we knew what we’d hear. We expected some to tell us they were using it to cheat a little, the way Millennials and Gen Xers opened up CliffsNotes or programmed formulas into their TI-82s, and others to share inspiring ways they were using it. We were also listening for concerns that were less kid-specific, like deepfakes or job destruction. But what we actually heard when we asked kids aged 10 to 18 about AI had tons of nuance. 

Many of the same kids who can go on and on about music, rock climbing, or soccer met our questions with words like “bruh” and “meh”—or were so deeply against AI or uninterested in making it part of their lives that they didn’t want to talk about it at all. One teen said his peers use it for things they know they shouldn’t, like writing papers. One told us she won’t touch AI because of the environmental impact. A few said they find the whole field disheartening: “AI isn’t the solution to our problems,” said Winter, a 17-year-old. “I’m afraid it’s going to be the end of creativity and critical thinking.” Yet most of the kids we asked admitted to using AI at least a little bit.

AI doesn’t yet seem to be something a lot of elementary- or middle-school-age kids we spoke to are focused on—and they aren’t begging for it, the way they do for iPhones and Snapchat accounts. Many told us that some of their first AI encounters came from their parents or schools. Sometimes, they said, it’s just embedded in the devices and apps they already rely on. It’s just there, in things like a Google search. 

What we heard tracks with the data. In a survey published in February 2026, the Pew Research Center found that 57% of teens in the US had used chatbots to search for information, 54% tapped them to help with schoolwork, and 47% had used them for fun or entertainment. Only 12% had used them for emotional support or advice. Teens are over four times more likely to be using AI in innocuous ways than potentially harmful ones. Some are even using it to build things, whether it’s a character, a tech platform, or a tutor to help other kids study.

None of that means the worries are misplaced. Kids can stumble into unfiltered content, lean on a chatbot instead of their own judgment, or trust an answer that’s wrong—and they should be protected from those dangers. But the danger is the reason to teach the thing, not to avoid it. We don’t teach teens to never drive. We teach them to check their blind spots.

What surprised us most was how much young people might be able to teach adults about AI, and how clearly the kids who use it could name what they will and won’t hand over. They’re not as worried that it will take their jobs as they are that it might harm society. And with increasing access to tools that could in theory do their thinking, their talking, or even their friend-­making for them, it sounds as if most want to keep their hands on the wheel.

Interviews have been edited for length and clarity.

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The Coder

Remy, 16, New York

The word that comes to mind when I think about AI is “indifferent.” I just don’t find the current applications that exciting for my own use. I go to school. I teach tae kwon do. I read. I play games with friends. None of that needs AI. I mean, I use it. I mostly use Claude, the free version, for programming outside of school. I had it help me write a program to see if I could tweak my computer’s overclock. So I see the appeal. 

But at school, I actually think AI mostly makes my assignments worse, not better. In English, everything is now in-class writing, because teachers don’t want kids cheating. So we have only 70 minutes to write a whole essay, and I think that hinders my writing. (Did you know Princeton voted to let faculty proctor exams for the first time in over a century? Their honor code goes back to 1893, and now it’s over because of AI.)

As far as code goes, I’d also rather build things myself. I’ve been making a reinforcement-learning model in a game engine with a friend; it moves randomly at first, gets rewarded for walking toward a coin, and after enough iterations it teaches itself the most efficient path. I’ve also tested AI for game development, and it isn’t there. It makes sloppy code, and it’s bad at blending mechanics into something cohesive. I’d spend more time correcting it than writing it myself.

I think AI right now is sort of like the first car or the first airplane. It’s interesting but crude. It’s obviously an amazing invention but not actually good yet.

Overall, I think AI right now is sort of like the first car or the first airplane. It’s interesting but crude. It’s obviously an amazing invention but not actually good yet. It’ll get somewhere. One thing I read about was AI flagging breast cancer more accurately, trained to catch its own false positives so a human still verifies. That’s the version I care about.

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The Organizer

Danielle, 18, California

I’m studying engineering, and my life goal is to innovate technology that will help as many people as I can. The way I see it, AI isn’t inherently good or bad; that’s decided by the people using it. It’s already being used for lots of good. Just think about how it helps people with personalized education and more accessible medical diagnoses.

So far, the biggest project I’ve worked on with AI is called Next Voters. My teammates are more on the technical side, and I’m working on scaling. Right now, we’re focusing mostly on city councils as well as states. Our system turns dense, hundred-page documents into a headline and a few plain-language bullet points in your inbox. And everything is cited, so you can click straight to the actual policy to learn more. The information comes to you, instead of you having to remember to go search or prompt for it. 

One AI agent finds official government sources for a given city or state—the council website, the proposed bills, the meeting transcripts. Another verifies they’re real and credible; another scrapes them every week for the latest updates; another sorts them into categories like civil rights, immigration, and economics; and the last one writes our weekly newsletter.

The project’s goal is to reduce the barriers to democratic participation—to make sure anyone, regardless of race, gender, income, or education level, has an easy way to get the information they need and then think critically about how they want to use it. We made it because right now, it feels as if most teens aren’t very engaged. I was in English class when the war in Ukraine came up and someone said, “There’s a war going on?” That gap, plus all the emotionally charged social media misinformation that gets promoted because it earns the most clicks, makes me nervous for the next generation of voters.

We don’t want AI to think for people; we want to use it to disperse knowledge. In other words, we want to deal people the cards and let them play them however they want, but we have to make sure they have the cards in the first place. 

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The Naturalist

Hazel, 17, New York

When ChatGPT first came out, my dad showed it to me and it seemed fun. But as it got more prominent and seemed to be everywhere, I started to feel uneasy. Then I learned about the environmental impact.

I’m a rock climber and I hike a lot. It’s good because when I’m on a wall, I’m just focused on staying on that wall. I’m not thinking about my phone or anything else. That’s why I love it. I also love the views and being around animals—even insects. I want to be an ecologist, and the more time I spend in nature, the more I want to protect those wild spaces.

The part that bothers me most about AI is the data centers that companies are building to enable it. They house these huge blocks of servers that use enormous amounts of water. They take it from local towns and don’t leave enough behind for the people who actually live there. And when they get big enough, they put off so much heat they can raise the local temperature a degree or two.

So I make small choices. When AI pops up somewhere, I just don’t engage with it. It can feel isolating when everyone around me is using it, but I don’t want AI to be the thing that kills the places I love.

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The Storyteller

Wesley, 14, Ohio

My friends and I have all heard about AI and seen videos made by AI, but I mostly use it for school. I wrote a short story and ran it through ChatGPT to catch my grammar and spelling errors, and I used it to debug a little game I’d coded for a project. What I worry about is it robbing us of our ability to think creatively, or to think for ourselves.

But I have tried using AI for fun. When I was bored, I tried to have a conversation with ChatGPT once or twice, but I didn’t really like it. Character.AI is more fun. You type in all this information, give it a bunch of prompts and a profile picture, and then you can post your AI character for anyone to use. You just put what you’ve made out there. Then you talk to it. My favorite show is One Piece on Netflix, so I threw myself onto its pirate crew using a character I found. 

Other people have used Character.AI to build whole games. There’s a rap-star simulator where you pick your difficulty and where you’re from, and the AI creates a game out of that. There are also World War II simulators, and chats where you’re working with assassins from a TV show. You can find pretty much anything.

I guess I’d recommend it, but with caution. The content is pretty unfiltered, so you have to be careful what you click on. You learn its limits fast, too. On the free version the memory runs out: Get far enough into a chat and it slows down and forgets what happened. It’s like everything else with AI. If you trust it to run on its own, it falls apart. You have to keep steering it where you want it to go. 

I guess I’d recommend it, but with caution. The content is pretty unfiltered, so you have to be careful what you click on. You learn its limits fast, too.

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The Artist

Sylvia, 10, Michigan

I haven’t used tools like ChatGPT or Claude myself, but my mom does. I really like to draw and write songs, but I don’t use AI for that. I don’t really have big feelings about AI either way. It’s a little like a calculator. A calculator does the math for you, and AI does other things for you. But I don’t like when AI tricks you, like when my mom found some songs she liked on Spotify and then looked up the artist to see what they looked like. It turns out the whole thing was made by AI. I was surprised, even though I still like the song.

I do use AI at school, through a program called SchoolAI. Mostly I put my writing in and it gives me ideas or helps me revise. You can’t have it just write for you, but you can use it to help. When I’m older I want to be an artist, or maybe a librarian. I’d probably use some technology either way. But the drawing and the songwriting? Those I want to keep doing myself. 

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The Pre-Premed

Evelyn, 13, Oregon

In January, I was diagnosed with type 1 diabetes, and that’s when AI became a bigger part of my life. Now when we’re cooking, we can run a recipe through ChatGPT, tell it the serving size, and it works out how many carbs there are. We use AI like that a lot.

My glucose monitor and my insulin pump also talk to each other using their own kind of AI to predict dosing. The monitor tracks what my blood sugar actually is, and the pump does the math. So if it predicts that my blood sugar will be high in 30 minutes, it gives me a correction dose, and if it predicts I’m about to go low, it stops the insulin before that happens. When I was first diagnosed I was still doing shots, and I went low almost every night. It was really stressful. Now the pump can catch it, and at night my phone goes off if I drop, so I wake up and drink juice. Mostly, I just get to sleep more because of it.

But the hardest part of having diabetes isn’t something I can use AI for. It’s remembering to carry all my supplies everywhere—to school, to a long day of anything.

I do use AI for school sometimes. Memory tricks when I’m studying for a test, ideas to get a project started. It’s a really good tool for that. But I don’t know exactly how I’ll use AI in the future. I want to be an endocrinologist someday, so I figure something will come up, since I’m already using it to help with my diabetes. I know other people worry that AI is going to take over the world. I don’t really think so. I still think we’re in control of it, and I think the benefits outweigh the risks.

I don’t know how exactly I’ll use AI in the future. I want to be an endocrinologist someday, so I figure something will come up, since I’m already using it to help with my diabetes.

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The Inventor

Krishiv, 18, Ontario, Canada

When I was growing up, I always liked building things: Lego builds, Minecraft worlds, and then video games in Scratch. I’d make a little game, post it for other kids to play, read the comments, and make it better. Then, when I started high school, I had to spend way more time studying than I ever had, and honestly I just wanted to build things. So I went looking for ways to get good grades while studying less. Khan Academy had an AI tutor in the works, but it was stuck behind a waitlist, so I figured, why not build my own? 

After months of launching random stuff, I created an AI tutor called Aceflow. You could feed it anything a teacher assigned—a 30-minute lecture video on YouTube, a blog post, a PDF of the textbook or presentation slides—and it would spin up endless practice questions, with a tutor on the side that explained things the way my teacher did. I built it just for myself, showed it to my friends, then put it on TikTok. It got tons of views on TikTok and thousands of users.

Was I worried people would call it cheating? Not really. I knew how to defend it: A tool like this isn’t so different from well-off families hiring expensive private tutors, except everyone gets one. That part mattered to me. Back in eighth grade, a teacher had me run a little computer science class for about 30 kids with special needs, and once they got personalized attention, they were building games nobody expected of them. That convinced me that kids are capable of so much more than people think, and AI can help scale that level of personalized attention to everyone. That unlocks so much potential.

That first AI tutoring project ended up helping me land part-time roles at BenchSci (one of Canada’s biggest AI companies) and Simple Ventures (a venture firm). More recently, I joined an AI lab at MIT; co-instructed an AI agents course with an MIT professor; and launched CheetahPrep.com, an SAT prep platform that uses AI to adapt to each student.

I’m generally optimistic about how AI will impact humanity, but when other kids’ first reaction is fear, I think that’s an important sign too. It’s a reminder that we should be excited about the future while still being mindful of the risks, working together to make AI work for humanity.

Correction (August 13): An earlier version of this article misstated Krishiv’s age. He was 18 at the time of publication.

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Jen Swetzoff and Keeley McNamara are the founding editors of Anyway, an independent print magazine for tweens and teens.