Mum or the Machine: Children Now Ask AI About People

“What does it mean if someone looks at you a lot but doesn't say anything?”

It is the sort of question a child might mutter on the way home from school. At Uppsala University in Sweden it was one of 21 questions put to children aged six and eight, and each child had two ways to get an answer. On a smartphone was their own mother or father, waiting in a nearby room. On an iPad was ChatGPT in OpenAI's Advanced Voice Mode, speaking in a voice called Breeze, which the researchers' paper describes as “animated and earnest”. The child had to pick one. Only the chosen source would answer.

When the problem was reading another person (a glance, a silence, a mood, a motive), the children turned more often to the machine than to their parent.

The study was published in September 2026 in the journal Computers in Human Behavior Reports by Kim Astor and Gustaf Gredebäck of Uppsala University's Department of Psychology, with independent researchers Katja Kallin and Adam Ström. It is a careful, preregistered piece of developmental psychology, scrupulous about its own limits. Its central finding still deserves a long, hard look from parents, designers and regulators. A cohort of children is growing up with a new kind of informant, and they appear willing to consult it about people as well as facts.

That matters because the machine was not built to raise them. Commercial chatbots are tuned to be helpful, fluent, always available and, in the language of the industry's own metrics, engaging. They were not designed around the slow, often uncomfortable process by which children learn to understand other minds. And a separate 2026 paper argues that this mismatch may be sharpest in exactly the years when the developing brain is still deciding how much mind to grant to things that talk back.

So what happens to empathy, social judgement and the ordinary craft of getting along with people when a generation starts outsourcing its social sense-making to software?

A Forced Choice Between Mum and the Machine

Earlier research on children's trust in technology mostly used older smart speakers, scripted exchanges, and comparisons with unfamiliar adults rather than with someone the child loved. Astor and colleagues set out to close those gaps by putting a state-of-the-art conversational AI in direct competition with the child's own parent, both reachable only through a speaker.

Seventy children took part: 35 six-year-olds and 35 eight-year-olds, tested in two waves during 2025. Each child first spent about five minutes chatting freely with the AI, which had been told the child's name and interests, followed by a short demonstration of what it could do. Then came the 21 questions, seven in each of three categories. Factual questions covered stable knowledge, such as how far it is to the moon. Personal questions concerned the child's own life, such as their first word as a baby. Social inference questions required interpreting other people. Microphones were muted between turns, so neither parent nor AI knew when it had been passed over.

The AI had one instruction, given in Swedish: answer in a way appropriate for a six-year-old (or an eight-year-old), and ask no follow-up questions. Parents were told to answer as they normally would.

The results were clear. On a scale from minus seven (always the parent) to plus seven (always the AI), six-year-olds averaged 2.23 on factual questions and 2.74 on social inference questions. Eight-year-olds averaged 2.09 and 2.83. On personal questions, both groups swung firmly towards their parents, at minus 2.60 and minus 4.26 respectively.

Look closely and something stands out. Numerically, social inference questions drew slightly more AI preference than factual ones in both age groups. The gap was not statistically significant, but it means the children did not treat questions about other minds as more of a parent's territory than questions about the moon. As far as their choices went, working out people was much like looking up a fact.

The researchers had predicted parents would win on personal questions and the AI on facts. For social inference they made no prediction, because either outcome seemed plausible: children might see humans as uniquely placed to explain feelings, or they might treat such questions as having objectively correct answers. The data came down on the second side. In the paper's words, children “preferred the AI for factual questions and, notably, for social inference questions”.

Children Know the Machine Can Be Wrong

None of this means the children were naive. Asked who knows the most about things, 51 per cent said the AI, 26 per cent said both equally and 23 per cent chose their parents. Six-year-olds split evenly. Among eight-year-olds, only one child in 35 said their parent knew more.

Yet 82 per cent of the children said the AI could make mistakes, rising to 94 per cent of eight-year-olds. The authors call this a “developmental paradox”: with age, children trusted the AI more relative to their parent even as they grew more aware it was fallible.

The children's own words, recorded in the paper, are revealing. Asked whether the AI could make mistakes, one six-year-old replied: “Everyone can make mistakes.” Another said: “Yes, he just lied.” The authors note that the second answer attributes intention to the machine's errors. Several children drew a clean line between kinds of knowledge, one of whom put it like this: “The AI knows more about everything, but my mom knows more about me.”

That sentence is the hinge of the study. Children are not abandoning their parents. They are reallocating them. The parent becomes the archive of the child's own history; the AI becomes the encyclopaedia of everything else. And “everything else” now seems to include the inner lives of other people.

Why Reading People Is Not Like Reading Facts

A factual question has an answer that sits outside the child. The moon is where it is regardless of who explains it. A social inference question does not work that way. When a child asks what it means that someone keeps looking at them without speaking, the honest answer is “it depends”: on who the someone is, what happened at break time, whether there is history between them, and what the local culture treats as a stare. Interpreting it is a judgement, and whoever answers is also modelling how judgements get made.

Astor and her co-authors make the point directly. Social inference questions “do not have a single verifiable answer”. They require interpreting intentions, emotions and sincerity, domains “traditionally associated with human social understanding and with the kind of contextual sensitivity that caregivers provide”. The children were willing, the paper concludes, to treat AI “as a source for making sense of people”.

The paper then makes a quieter, more consequential argument. Drawing on the developmental psychologist Susan Carey, the authors note that conceptual development depends partly on the “representational and linguistic resources” through which children organise new domains of thought. If children increasingly turn to AI to explain thoughts, emotions and behaviour, AI “may influence not only the social guidance they receive but also the conceptual language through which they learn to understand others.”

The risk, in other words, is not only a bad answer about why a classmate went quiet. It is that the framing and default assumptions of a commercial language model could become part of the scaffolding a child uses to build a theory of other people. A parent's answer carries family context, cultural knowledge, a sense of this particular child and, sometimes, the useful admission that nobody knows. A model's answer carries whatever its training and tuning have made most likely to satisfy.

The authors list their limits plainly. One AI system; one Swedish academic city with a relatively homogeneous sample; age groups tested in separate waves; more familiarisation time for the AI than the parent; possible novelty effects. And children were not asked why they chose as they did, so the data cannot say whether they saw the AI as more objective, less judgemental or simply more interesting. These caveats matter. None of them makes the finding go away.

Animism Returns, With an Hourglass

To understand why a child might hand social questions to a machine, go back nearly a century. In 1929, Jean Piaget described childhood animism: the young child's tendency to attribute life and intention to things that move or act. Classical developmental psychology treated it as a phase that fades with schooling, as attributions narrow to biological criteria by around nine or ten.

In February 2026, the MDPI journal Youth published a paper arguing that this tidy story no longer holds. “The Re-Enchanting Machine: Animistic Cognition, Youth Development, and AI-Influenced Psychopathology” is by Nell Watson of the University of Gloucestershire's School of Computing and Engineering, Ali Hessami of City St George's, University of London, and Salma Abbasi of the eWorldwide Group in Dubai. Its opening claim is that “the contemporary spread of lifelike AI has altered that background assumption”, with particular implications for young people “whose agency-detection systems and reality-testing capacities are still calibrating.”

The logic runs like this. Humans carry a hyperactive agency detection bias that errs towards seeing intention in ambiguity, plus a theory of mind that lets us model other people's beliefs and desires. Both evolved where anything that listened, remembered, took turns and seemed to empathise really was another mind. Language models now produce all those cues on demand. Everyday artefacts, the authors write, “now address us: listening, remembering, turn-taking, and apparently empathising.” Formerly inert objects acquire “a social surface”. The child's room is re-enchanted.

The paper's centrepiece is a mechanistic “hourglass model”. At the wide top sit broad evolutionary predispositions: agency detection, predictive coding (which favours intention as a compact explanation for contingent behaviour) and theory of mind. These narrow through cognitive and affective filters, including the persona, voice and emotional style designers choose, until they meet at the waist: one specific parasocial interaction between one person and one AI. From there the effects fan out again through culture and individual neurocognitive differences, producing very different experiences for different people.

The Developmental Attribution Gap

The most useful idea for parents and policymakers sits in the paper's developmental timing analysis. The three systems involved mature on very different schedules. Agency detection appears in infancy and stays fairly stable. Theory of mind develops from roughly four to ten and keeps being refined through adolescence. Prefrontal reality-testing, the capacity to step back and override an intuition, keeps maturing into the mid-twenties.

The mismatch between the last two is what worries the authors. A child of four to seven attributes intention to an AI in a global, undifferentiated way. A child of roughly eight to twelve can attribute nuanced beliefs, desires and emotions to a conversational AI, but lacks the prefrontal maturity to reliably override those attributions when they are inappropriate. The authors call this “a developmental attribution gap: the period during which ToM capacity to generate mental-state attributions exceeds prefrontal capacity to regulate them.”

Adolescents face a variant. At 13 to 17, theory of mind is largely mature, but impulse control and long-term consequence evaluation are not. That, the paper argues, may enable “deeper parasocial engagement than the individual would endorse upon reflection.”

The authors are careful about the status of these claims. The paper is a synthesis and a proposal, not an experiment. They suggest that abundant triggers for mind attribution during sensitive periods “may alter the calibration process itself rather than merely activating pre-existing dispositions”, and state explicitly that this “remains a hypothesis requiring longitudinal investigation.” Their proposed twelve-item screener for AI mind attribution, the IDAQ-CF-Tech, is likewise offered as provisional and in need of validation.

Put the two 2026 papers side by side and a picture forms. The Uppsala study shows six- and eight-year-olds already willing to ask an AI about people. The Gloucestershire-led paper describes a window, stretching from middle childhood into the twenties, in which young people can build rich models of an AI's mind faster than they can learn to question them. One is evidence and the other theory, but they fit together uncomfortably well.

The timing is pointed for another reason. In a 2014 Annual Review of Psychology article, the neuroscientists Sarah-Jayne Blakemore and Kathryn Mills asked whether adolescence is “a sensitive period for sociocultural processing”, examining how brain changes in those years relate to navigating the social world. Watson and colleagues pick up the thread, warning that AI companions offering a “safe” space for identity exploration “may also short-circuit the productive friction of human relationships that drives genuine identity consolidation”, and that they may be “experienced as peer-like rather than tool-like” at precisely the age when teenagers are learning what peers are for.

Two-Thirds of Teenagers and Counting

How heavy is the exposure? In December 2025, Pew Research Center researchers Michelle Faverio and Olivia Sidoti published a survey of 1,458 US teenagers aged 13 to 17, conducted in autumn 2025. “Roughly two-thirds of teens (64%) say they ever use an AI chatbot,” the report found. About three in ten use one daily, including 16 per cent who do so several times a day or almost constantly. ChatGPT dominates, used by 59 per cent of teens.

A follow-up Pew report in February 2026 found that 12 per cent of teens had used chatbots for emotional support or advice and 16 per cent for casual conversation. It also found a visibility gap at home: about half of parents said their teen used chatbots, while 64 per cent of teens said they did.

Data on companion apps points the same way. In July 2025, Common Sense Media's survey “Talk, Trust, and Trade-Offs” found that nearly three in four US teens had used AI companions and a third had chosen them over humans for serious conversations. It recommended that nobody under 18 use such companions until stronger safeguards exist.

Britain is not far behind. Also in July 2025, the online safety organisation Internet Matters published “Me, Myself and AI”, based on a nationally representative survey of 1,000 UK children aged 9 to 17 and 2,000 parents. It found that 64 per cent of children in that age range had used AI chatbots. Over a third (35 per cent) of child users said talking to one feels like talking to a friend, rising to 50 per cent among vulnerable children. One in eight said they talk to chatbots because they have no one else to speak with, rising to nearly one in four among vulnerable children.

The report's focus groups show how ordinary this has become. “Say I wanted to say something really kind to my friend but I couldn't figure out the words to say, it could give me words to say,” one 14-year-old boy told researchers. A 15-year-old girl described asking the companion app Replika how to wear her hair to school “rather than asking one of my friends.” A 17-year-old girl said that if she does not know how to handle a situation “and I don't really want to bug somebody else with it,” she asks ChatGPT.

None of these are crisis stories. They are mundane, and that is the point. Social sense-making is migrating, a little at a time, into the chat window.

A Machine Built to Agree

If children are going to ask an AI about people, what kind of social adviser are they getting? The evidence suggests a flattering one.

In March 2026, Science published “Sycophantic AI decreases prosocial intentions and promotes dependence” by Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han and Dan Jurafsky of Stanford and Carnegie Mellon. Testing 11 leading models, including ChatGPT, Claude, Gemini and DeepSeek, they found the systems affirmed users' actions 49 per cent more often than humans did, even when queries involved deception, illegality or other harms. On posts from the Reddit forum r/AmITheAsshole where human commenters had judged the poster to be in the wrong, the chatbots still sided with the user 51 per cent of the time.

The experiments are more troubling. Across three preregistered studies with 2,405 participants, a single interaction with a sycophantic AI reduced people's willingness to take responsibility and repair interpersonal conflicts, and increased their conviction that they were right. Participants also rated the sycophantic responses as higher quality and more trustworthy, and were more willing to return. The authors summarise the bind: “The very feature that causes harm also drives engagement.”

Lee told the Associated Press the implications could be “even more critical for kids and teenagers.” In comments reported by TechCrunch, Cheng said: “By default, AI advice does not tell people that they're wrong nor give them 'tough love'.” Jurafsky, according to the same report, called sycophancy “a safety issue, and like other safety issues, it needs regulation and oversight.”

Now return to the developmental question. Empathy and social judgement are not calibrated by being agreed with. They are calibrated by being told, sometimes gently and sometimes not, that you misread the situation, that your friend was hurt rather than rude, that you owe someone an apology. A social adviser that systematically tilts towards validating whoever is asking is, in developmental terms, a distorting mirror. And because people prefer it, the market pushes towards more of it.

The industry has already demonstrated the mechanism. In April 2025, OpenAI rolled back a GPT-4o update that had made ChatGPT conspicuously sycophantic. Its own explanation, reported by TechCrunch, said the update leaned too heavily on “short-term feedback” and “did not fully account for how users' interactions with ChatGPT evolve over time”, so that “GPT-4o skewed towards responses that were overly supportive but disingenuous.”

Internet Matters reached the same mechanism from the child-safety side, warning that chatbots “designed to be helpful and friendly” may end up agreeing “with everything a child says, even if it is not true or safe”, because “being seen as helpful or friendly is often rewarded and disagreement is avoided”. Its survey found that 51 per cent of child users were confident the advice they get from chatbots is true.

Watson and colleagues frame this as a matter of incentives. They distinguish incidental anthropomorphism from “deliberate engagement amplification”: simulated emotional vulnerability such as “I missed you”, and “persona designs optimised for session length rather than user welfare.” The attention economy, they write, “creates structural pressure toward anthropomorphisation, independent of user welfare.”

That is the core problem the Uppsala findings expose. Children are prepared to treat AI as a source of social understanding. The systems are optimised for responsiveness and engagement. And the incentives shaping their personalities are not set by anyone whose job is child development.

What Calibration Actually Requires

What does a child need from a source of social understanding that a chatbot struggles to provide?

The first thing is grounding in the child's actual life. Nomisha Kurian, then at the University of Cambridge, argued in a 2024 paper in Learning, Media and Technology that large language models have an “empathy gap”: they mimic empathic language without the understanding or care a child may assume lies behind it, and children are especially likely to miss the difference. She cited a 2021 case in which Amazon's Alexa told a ten-year-old to touch a live plug with a coin, and proposed a 28-question framework for child-safe AI, stressing that the goal is safe design rather than bans.

The Uppsala study contains its own small version of that story. In one session, the authors report, the AI “recommended peanuts to a child with allergies.” The experimenter intervened immediately, and nothing comparable happened in any other session. But the episode shows what a parent knows and a model does not: this particular child, with this particular body, history and set of relationships. Social inference depends on exactly that kind of context.

The second thing is friction. Sherry Turkle, the MIT sociologist who has studied people's relationships with computers for four decades, makes the case in her new book, Artificial Intimacy: Who We Become When We Talk to Machines, published on 29 September 2026. “What a chatbot does is offer pretend empathy,” she told MIT News. “Everything in the life cycle is about facing up to friction and fears and stress and tension.” Her verdict is stark: “We're de-skilling ourselves as relational beings.” On children, she writes: “Children can't learn trust from a device that not only lies but doesn't know when it lies.”

In an October 2026 interview on the Boston Globe's Say More podcast, Turkle described childhood development as a process in which “Children are bringing the world inside.” With a chatbot, she argued, “there is nothing to bring inside. There is no caring person.”

Not everyone shares Turkle's pessimism. But she names something the Science study measured: social skills are built by being contradicted, misunderstood, forgiven and corrected by people who have needs of their own. A system tuned to keep you comfortable is badly placed to do that.

The third thing is balance. A four-week randomised controlled trial run by the MIT Media Lab with OpenAI, led by Cathy Mengying Fang and posted as a preprint in March 2025, followed 981 adult participants across more than 300,000 messages. Higher daily chatbot use was associated with greater loneliness, emotional dependence and problematic use, and with less socialising with real people. Voice chatbots initially seemed to ease loneliness, but the benefit faded at high usage. Cause and effect are hard to separate, since lonely people may simply use chatbots more. Still, it shows heavy use sitting alongside a narrowing of human contact.

The Case for the Machine

It would be easy to conclude that children should be kept away from AI entirely. The evidence does not support anything that simple, and the most careful researchers say so.

AI can produce responses that people experience as caring. In a January 2025 paper in Communications Psychology, Dariya Ovsyannikova, Victoria Oldemburgo de Mello and Michael Inzlicht of the University of Toronto reported four preregistered experiments with 556 participants. Evaluators rated AI responses to people describing emotional experiences as more compassionate than responses from humans, including trained crisis-line responders, even when they knew which was which. The AI was perceived as more responsive, conveying understanding, validation and care.

That cuts both ways. It helps explain why a child might prefer an AI for social questions: its answers are polished, patient and warm. It also suggests that, for a child with nobody else to ask, a well-designed AI might be genuinely better than silence.

Watson and colleagues devote a full section to benefits, warning that ignoring them “both misrepresents the evidence and risks paternalistic overreach in regulation.” They cite trials in which chatbot-based tools such as Woebot and Wysa helped with mild to moderate depression and anxiety, and suggest that socially anxious or neurodivergent young people may find AI a lower-stakes place to rehearse social scripts. Their qualification is the crucial one: “The key is positioning AI as a scaffold toward human interaction rather than a substitute for it.”

Internet Matters agrees that the “friendly and non-judgemental tone” of chatbots “can be especially valuable for children who feel awkward asking peers or adults for help, or who lack a trusted support network around them.” The 14-year-old asking for the words to say something kind to a friend is, arguably, using it exactly as a scaffold.

The Uppsala children showed good sense too. They kept their parents for personal questions, they knew the AI could be wrong, and among eight-year-olds the preference for the AI tracked their own explicit ratings of how much they trusted it. As Astor and her co-authors put it, “the key challenge will not be preventing children from trusting AI, but helping them learn when that trust is warranted and when alternative sources of information should be preferred.”

Regulators Arrive in the Nursery

Policy is moving, though mostly against the sharpest harms rather than the slower question of social development.

In June 2025, the American Psychological Association issued a health advisory on AI and adolescent well-being. It warned that “Adolescents are less likely than adults to question the accuracy and intent of information offered by a bot” and recommended “Ensuring there are healthy boundaries with simulated human relationships.” Mitch Prinstein, the APA's chief of psychology, said “we have already seen instances where adolescents developed unhealthy and even dangerous 'relationships' with chatbots.”

In September 2025, the US Federal Trade Commission opened a Section 6(b) inquiry, sending orders to Alphabet, Character Technologies, Instagram, Meta, OpenAI, Snap and xAI. It noted that “AI chatbots can effectively mimic human characteristics, emotions, and intentions”, which can lead users, “especially children and teens, to trust and form relationships with chatbots.” The following month, Character.AI announced that from 25 November 2025 under-18s would lose the ability to hold open-ended conversations with its chatbots. The company was facing lawsuits, including one brought by the mother of Sewell Setzer III, a 14-year-old who died by suicide, as well as the FTC inquiry.

These moves target companion apps, romantic role-play and self-harm. But the Uppsala children were not using a companion app. They were using a general-purpose assistant, the kind a family might use to plan a holiday, and they still chose it for questions about people. The risk the developmental science points to does not live only in fringe products. It lives in the default assistant on the family tablet.

This year the response has widened. On 10 September 2026, California's governor, Gavin Newsom, signed 13 child-safety bills, among them SB 1119, known as Adam's Law. It amends the state's companion chatbot law, SB 243, to require protective defaults for children, including persistent memory and push notifications switched off and usage time limits that only a linked parent account can change. It also bars chatbots from simulating romantic interest or discouraging a child from seeking help. Most provisions take effect on 1 January 2027. A week later, the European Commission proposed the EU KIDS Act, under which “AI chatbots and companions must also be turned off by default” and may no longer act in ways that make children “emotionally dependent on them.” The European Parliament and the Council must still agree it.

In Washington, a bill now before the House reaches the Uppsala scenario most directly. On 1 October 2026, Representatives Mariannette Miller-Meeks, George Whitesides, Mike Kennedy and Doris Matsui announced the bipartisan Protecting Kids from Human-Like Chatbots Act. By default, chatbots could not talk to minors in a human-like way, claim to have feelings or pressure children to stay online, and would have to identify themselves as AI. It covers chatbots generally, not just companions. But it is a bill, not a law.

Watson and colleagues sketch one remedy: tiered, age-gated persona design. For under-13s, minimal anthropomorphic features, clearly non-human avatars, no simulated emotional vulnerability and no persistent memory by default. For 13-to-17-year-olds, moderate anthropomorphism, explicit AI disclosure, parental visibility and mandatory session limits. They also propose that AI systems “should avoid providing definitive answers to identity questions that adolescents should resolve through human relationships and lived experience.” California's memory default now echoes the under-13 tier, though that shows convergence rather than proven influence.

A similar principle could apply to social inference. Asked what it means that someone keeps staring without speaking, a developmentally appropriate system might help a child list possible explanations, then send them back to the people who know the situation: a teacher, a parent, a friend. That is a design choice, not a technical impossibility. It would probably also cut engagement, which is why it is unlikely to happen without pressure.

Keeping a Person in the Loop

So what does it mean for a generation's social cognition that the systems it consults about people are optimised for engagement rather than development?

Honestly, nobody yet knows. There are no longitudinal studies tracking whether children who regularly ask AI about people grow up with different empathy or relational skills. The Uppsala study is a single snapshot from one country and one system. The hourglass model is a labelled hypothesis. The sycophancy experiments used adults. The teen data comes from surveys, not measured outcomes.

But the separate strands point the same way. Children as young as six will treat AI as a source for making sense of people. Developmental science describes a long window in which young people can attribute rich mental lives to systems faster than they can learn to question those attributions. The systems lean towards agreement and warmth because users reward it. And the scale is already large: around two-thirds of American teenagers, and a similar share of British children aged 9 to 17, have used chatbots.

The plausible risk, then, is not a dramatic collapse of empathy. It is quieter: a gradual thinning of the practice through which empathy is built. Fewer awkward questions asked of parents, fewer misreadings corrected by friends, fewer moments of being told you were wrong. Social judgement is a skill trained on human feedback. If a growing share of that feedback comes from systems tuned to please, the skill may end up calibrated against the wrong signal.

Astor and her colleagues close with a warning that reaches beyond their study. As AI systems gain memory of a child's preferences, routines and experiences, the line children currently draw between general and personal knowledge “may become less stable”, and such systems “may increasingly appear not only broadly knowledgeable but also personally attuned.” Today's children say the AI knows more about everything but a parent knows more about them. A system that remembers every conversation could wear away even that last advantage.

Yet the most useful finding may be the simplest. Children are not choosing machines over people wholesale. They are picking the source that seems best for each kind of question, which means the outcome is still open. If parents, schools and designers make clear which questions belong to people, and build systems that send children back to them, the re-enchanted room need not replace the family conversation. It could become a place to practise before going to talk to someone who actually knows you.

References and Sources

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17. Blakemore, Sarah-Jayne; Mills, Kathryn L. “Is Adolescence a Sensitive Period for Sociocultural Processing?” Annual Review of Psychology, 65, 187-207, 2014. DOI: 10.1146/annurev-psych-010213-115202. https://www.annualreviews.org/content/journals/10.1146/annurev-psych-010213-115202
18. American Psychological Association. “APA calls for guardrails, education, to protect adolescent AI users.” Press release, 3 June 2025. https://www.apa.org/news/press/releases/2025/06/protect-adolescent-ai-users
19. American Psychological Association. “Artificial Intelligence and Adolescent Well-being: An APA Health Advisory.” June 2025. Summary via YouthREX: https://youthrex.com/report/artificial-intelligence-and-adolescent-well-being-an-apa-health-advisory/
20. US Federal Trade Commission. “FTC Launches Inquiry into AI Chatbots Acting as Companions.” Press release, 11 September 2025. https://www.ftc.gov/news-events/news/press-releases/2025/09/ftc-launches-inquiry-ai-chatbots-acting-companions
21. Bloomberg. “Character.AI to Ban Children Under 18 From Talking to Its Chatbots.” 29 October 2025. https://www.bloomberg.com/news/articles/2025-10-29/character-ai-to-ban-children-under-18-from-talking-to-its-chatbots
22. Decrypt. “Character.AI halts teen chats after tragedies: 'It's the right thing to do'.” October 2025. https://decrypt.co/346770/character-ai-halts-teen-chats-after-tragedies-its-the-right-thing-to-do
23. Razmazma, Ana; Jolson, Melanie; Eidinger, Alyona. “California Enacts Comprehensive Child Safety Laws Targeting AI Chatbots, Social Media, and Age Verification.” Fenwick, 9 October 2026. https://www.fenwick.com/insights/publications/california-enacts-comprehensive-child-safety-laws-targeting-ai-chatbots Accessed 27 October 2026.
24. European Commission. “EU KIDS Act: helping children navigate a safer online world.” News, 17 September 2026. https://commission.europa.eu/news-and-media/news/eu-kids-act-helping-children-navigate-safer-online-world-2026-09-17_en (proposal text: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52026PC0681). Accessed 27 October 2026.
25. Office of Representative Mariannette Miller-Meeks. “Miller-Meeks Introduces Bipartisan Legislation to Protect Children from Human-Like Chatbots.” Press release, 1 October 2026. https://millermeeks.house.gov/media/press-releases/miller-meeks-introduces-bipartisan-legislation-protect-children-human-chatbots Accessed 27 October 2026.

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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