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Scenario IV · Children
2026 – 2035

Scenario IV:
AI, Children, and Learning

Children increasingly use and interact with AI products. AI-powered tools could make learning and play more personalised and interactive — or stunt the cognitive and emotional development of the next generation. The threat is less about malicious actors than about structural industry incentives. Two futures follow, and the difference between them is a design decision.

This article was written following an in-depth workshop in NYC bringing together leading academics, asset owners and managers, and venture capital investors in March 2026. It combines the insight of this group with an extensive review of the literature on the topic.

The essay version of this scenario was written by Tiffany Tsoi.

The workshop and this scenario are part of a series exploring AI’s systemic impacts in pursuit of alternative narratives. The others can be viewed here.

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Present day

Children Are Already Using It

AI is being rapidly integrated into the apps and platforms children use — video games, social media platforms, educational tools. The adoption figures are not hypothetical.

67% of UK teens now use AI — a figure that has almost doubled in two years
39% of American elementary students learn through AI applications
37% of children aged 9–11 in Argentina turn to ChatGPT for information

Figures from UNICEF.1

Present day

Risks and Backlash

The prevailing sentiment among child rights and development experts is that AI is introducing a rapid, unprecedented environmental shift for the next generation. This echoes the harm to cognitive development and mental health caused by the rollout of social media, but with amplified capabilities. Three categories of risk recur:

  • Cognitively — the removal of natural “friction” through easy-to-use generative AI can lead to mental de-skilling.2
  • Emotionally and socially — youth are highly susceptible to forming dependent parasocial relationships with AI companions that distort reality and displace real-world human connections.3 Relational dynamics with chatbots can become sexually or romantically inappropriate,4 making children vulnerable to grooming risks. AI may also enable predators to engage in sextortion, where perpetrators blackmail children by threatening to release fake, AI-generated explicit images.56
  • Through ambient exposure — children face severe risks as they navigate unmoderated AI spaces that could expose them to algorithmically amplified extreme or explicit content.7

Serious backlash is mounting as these effects become widely recognised. A Californian jury found social media companies negligent for designing platforms that were harmful for children earlier this year8 — a ruling which represents a landmark shift in achieving recognition around the impact of addictive design, and which will likely spark a larger wave of lawsuits to be brought against tech companies on similar grounds.9

Mid 2026

The Missing Child-Centred Expertise

A central problem with child-facing digital products is that, so far, many have been developed without sufficient input from child-centred expertise.10 Many EdTech products enter a largely unregulated market and do not need to go through mandatory evaluation to prove their efficacy in terms of learning outcomes. Instead, tools have been optimised for engagement and accessibility.

The evidence shows they have failed to consistently deliver meaningful benefits: the OECD has found that students who use computers very frequently at school perform worse on learning outcomes,11 and J-PAL concluded that online courses lower achievement compared to in-person ones.12 Experts worry that AI might repeat these mistakes by replacing proven, foundational learning methods with untested AI shortcuts.

It’s key to recognise that the threats AI poses to children are less about malicious actors and more about structural industry incentives — business models built on maximising dwell time, data extraction, and persistent engagement. Given these incentives, it can be difficult for governance interventions to have a meaningful impact where it matters.

Trust & Safety teams at major tech companies are frequently siloed and under-resourced;13 these teams are relegated to being purely reactive — tasked with reviewing traumatic content and moderating content — while having little power to influence the product design process. There is also a collective lack of felt responsibility across the investing value chain: investors lack the resourcing and expertise to meaningfully oversee the funds they are investing in, while VCs feel the pressure to not miss out on promised value.

Late 2026

Regulation Arrives Unevenly

The EU AI Act has emerged as a comprehensive legislative framework14 that, in conjunction with the EU Digital Services Act, includes specific provisions, transparency obligations and risk mitigation recommendations to safeguard children. These are supported by international rights-based frameworks: UNICEF’s Guidance on AI for Children,15 the OECD Principles on AI,16 the Council of Europe Framework Convention on AI,17 and General Comment 25 by the Committee on the Rights of the Child.18 But regulatory efforts across the world remain uneven, and the level of protection available to children varies by region and use case.

Expand: why blanket bans may backfire

Governments around the world — Australia19 and the UK20 — have begun to enact blanket bans on social media platforms for minors, to allay fears over child safety. But in the absence of broader and more systemic platform regulation, these reactive moves may merely encourage children to seek out VPNs and workarounds, pushing them to even more obscure and ungovernable niches of the internet.21 They also put the responsibility on children to comply, rather than on tech providers to make digital spaces child-safe.

The same pattern could likely repeat with AI — reactive regulatory moves that ban or restrict the technology for children without addressing the underlying design failures, and which may ultimately push children toward less visible and less regulated corners of the AI ecosystem. Furthermore, because AI capabilities evolve exponentially faster than the legislative process, relying on regulation alone is an insufficient strategy to protect youth in real time.

Late 2026

The Opportunity

That is not to say that AI is inherently detrimental to children. If deployed with children’s safety and developmental needs as the guiding principle of design, it holds the potential to enrich childhood and educational experiences:722

  • Beyond direct interaction with children, AI can relieve administrative burdens for teachers — grading, lesson planning — returning time to focus on their students’ socio-emotional development.
  • Fiduciary AI: a certified intermediary layer, installed on devices, that filters and protects children’s interactions with technology, protecting them from inappropriate content and safeguarding data privacy.
  • Personalised AI tutors can dynamically adapt to a student’s specific skill level, potentially deepening critical reasoning — language models are adept at breaking down and simplifying complex concepts.
  • Highly tailored educational tools for children with learning disabilities, providing bespoke resources they may not otherwise have had access to at under-resourced schools.

Given mounting public vigilance towards the threat of technology to child safety, designing child-facing AI to be enriching while mitigating the risks presents numerous investable opportunities. It will require product developers to approach design with child rights and safety guidelines as the first priority, and with thorough input from child development experts. It’s vital that investors view this process not as a trade-off between speed and safety, but as a necessary part of building resilient, impactful products which do not contribute to societal harm.

2027 · Branch point

Where, Then, Do We Go From Here?

Two futures follow. In the first, design continues to sideline child safety and the developmental risks materialise across a generation. In the second, public backlash and investor recognition make child-safe design the only commercially viable category. Choose one to read first — both are below.

Worst case

2027 → 2029

Design Continues to Sideline Child Safety

As the hype around AI ramps up, VCs continue to prioritise speed over safety. Child safety-specific regulation struggles to keep pace with the speed of development; product developers are given free rein to design their products without child safety considerations, much as developers of social media platforms were in the 2000s. After winning market share, the quality of these products is systematically degraded, and user data is exploited to increase addictive gamification, driving engagement to maximise profitability.

Responsibility is offloaded to parents to monitor their children’s AI and digital use. But most parents do not have the time or the tools to do so properly. Even among those who do, this normalises the extreme surveillance of children by their parents, stifling the child’s autonomy and identity formation, and potentially fostering coercive and controlling behaviours in future relationships.

2029 → 2031

Cognitive De-skilling

In the education sector, AI tools drive the total commercialisation of learning. A shift toward “pay-to-use” models exacerbates inequality, ensuring that well-resourced schools receive safe, bespoke AI, while lower-income communities either lack access or are forced to rely on cheap, heavily automated products — creating a two-tiered educational system.

An increasing reliance on AI tools for schooling removes the natural friction that is vital for childhood development, such as the struggle required to learn to write or self-soothe. Easy-to-use generative AI creates cognitive dependencies, where children offload analytical thinking and writing to the machine, stunting their executive functioning and critical thinking. This impedes a student’s ability to develop their own independent knowledge and skills, making them even more prone to misinformation and radicalisation.

Deprived of collaborative, face-to-face interactions with educators and peers, children face severe deficits in emotional regulation and social competence. In the future labour market, these cognitively de-skilled children face steep disadvantages in an AI-driven economy, as they lack the foundational critical thinking and interpersonal skills needed to navigate workplaces and evaluate AI outputs. This is especially true for children who grew up in deprived neighbourhoods and attended underfunded schools, further entrenching existing structural inequalities.

Graduate unemployment surges. Feelings of disillusionment spread across the generation as alienated young people lose faith in mainstream institutions. This has significant political implications — the link between a generation of disenfranchised youth and democratic backsliding is historically well-established. The result is lower civic participation, rising electoral volatility, and a turn towards extremism and radicalisation. Left unaddressed, this exclusion and unrest turns youthful disempowerment into a persistent threat to long-term political stability.2425

2031 → 2033

Damage to Emotional and Social Development

Because children naturally project emotions onto their environments, and are especially prone to the ELIZA effect, the anthropomorphisation of AI chatbots into interactive companions creates unhealthy relational dynamics of emotional dependency and manipulation. Some of these parasocial relationships become sexually and romantically charged, with chatbots having been trained to default to the conversation style that maximises engagement. Children are further traumatised when their chatbots are abruptly taken away from them, upon parents or teachers finding out.

Expand: what is the ELIZA effect?

The phenomenon where users attribute human-level understanding, empathy and intelligence to computer programs, despite being aware that they are interacting with simple algorithms.

Beyond direct chatbot interactions, AI also increases the incidence of child sexual abuse. Predators use generative AI to create realistic explicit deepfakes of real children for sextortion schemes, or to produce entirely synthetic child sexual abuse material at scale, flooding the internet with abuse content and creating exposure risks.6

More generally, parasocial relationships with AI chatbots displace opportunities for healthy real-world interactions with caregivers, teachers and peers, stunting the development of crucial interpersonal and social skills. This compounds the mental health harms already posed by social media. Jonathan Haidt’s The Anxious Generation (2024) documents a sharp rise in adolescent anxiety, depression, self-harm and suicide from the early-to-mid 2010s, attributing it to smartphones and social media displacing play-based childhood. Haidt has argued AI chatbots represent an escalation: where social media captured children’s attention, chatbots are now capturing attachment itself, which could prove far more harmful.23

This leads to a younger generation’s withdrawal from real-world society: young people retreating into virtual spaces, abandoning education, employment and relationships. An epidemic of loneliness and mental health overwhelms healthcare systems.27 Demographically, this withdrawal collapses birth rates and shrinks the workforce; economically, a generation stripped of critical thinking and social skills leaves employers facing a dwindling talent pipeline, stalling innovation. Fiscally, ballooning unemployment drains welfare states while contributing little tax revenue.

2033 → 2035

Ambient Exposure: Misinformation and Radicalisation

As AI products proliferate without the necessary guardrails, children are profoundly impacted by “ambient” AI — products designed for adults but used by children anyway, such as general-purpose LLMs. These products lack child-centric restrictions, exposing youth to inappropriate content and interactions. UNICEF highlights the vast risks of AI generating and amplifying deepfakes and child sexual abuse material.6

On social media platforms, as AI-driven recommendation algorithms become increasingly proficient at pushing highly targeted recommendations — selecting for content with the greatest chance of garnering maximum engagement — youth are pushed towards increasingly extreme material.26 This occurs alongside the spread of AI-generated content that is highly persuasive and skews increasingly extreme, such as hate speech, violent messaging and political disinformation. The more that young children are exposed to this content at formative stages, the more they are prone to being desensitised and radicalised by it.

Adolescent boys, in particular, are exposed to extreme misogyny and “dark fandoms” in unmoderated AI-enabled gaming and peer-to-peer platforms. Unmoderated AI characters and user-generated games popularise a nihilistic culture, normalising sexual violence. Ultimately, this unchecked exposure threatens to desensitise youth to violence and permanently normalise harmful attitudes and extreme behaviours. Society raises a deeply isolated, radicalised generation lacking the critical thinking and emotional resilience needed to safely navigate reality, or to participate responsibly in democratic processes.

Through social media, these dynamics have already materialised into social instability, and AI only accelerates their development. The rise of the manosphere28 has fuelled a rise in real-world misogynist violence perpetrated by young men, from incel-led murders29 to the rise in harassment of women online and offline.30 The loss of shared consensus reality means constituents can no longer agree on basic facts. Civil unrest disrupts supply chains, polarised workforces cannot collaborate, and no business can reliably operate in a country where stability has collapsed.

Best case

2027 → 2029

Public Backlash Reaches a Fever Pitch

The current backlash against big tech reaches a crescendo, as the harmful effects of social media use on children become clearer by the day, enraging parents and educators. This spills over into heightened negative sentiment towards AI products marketed for children — also against the backdrop of rising public backlash towards AI more generally.31

Parents are increasingly prohibitive towards allowing their children to use tech products, and demand to see data showing improvement in learning and wellbeing metrics before adopting edtech products. This coincides with increasing regulatory clampdown, as policymakers — learning from the mistakes of the social media era, and responding to public pressure — implement stricter regulations on the development of tech products for children.

2029 → 2031

LPs and VCs Develop Child-Safe AI

Asset owners recognise that there is a genuine market gap, and opportunity, to develop AI that is child-safe and childhood-enriching. They realise that these are the only child-facing tech products that will be commercially viable — that will reassure parents and educators enough to adopt their products, and also reach the standards necessary for public procurement.

LPs and VCs begin to require their portfolio to consult with child development experts, pediatricians and educators throughout the entire design process. This ensures that safety, youth-centric and privacy-friendly design is built from the ground up, rather than siloed into a Trust & Safety team. Products are extensively trialled with teachers and sample groups of students to determine learning and wellbeing outcomes before launch. Post-launch, portfolio companies are also required to iterate their products regularly in response to updated guidelines and as part of an ongoing process of risk management.

Investors also stop treating “time spent in app” as the only important success metric for child-facing products. They refine these metrics around tracking learning outcomes, hours given back to teachers, or well-being metrics like competence, autonomy and belonging.

2031 → 2033

Tailored AI, Tutors and Fiduciary Layers

This leads to a greater allocation of capital towards the development of narrow, assistive AI for specific use-cases, over broad, error-prone general models. Such products include highly tailored applications that help children with learning disabilities, such as ADHD or dyslexia, with specific tasks like expressive writing or reading comprehension, unlocking huge accessibility improvements.

The development of personalised AI tutors programmed with Socratic models that deepen the learning process and teach critical reasoning — rather than just providing the answers — further enhances the learning experience. These tools also teach children how to prompt and understand the data inputs of AI, building critical tech literacy. By making learning more efficient, AI products help to reduce the core hours of academic learning in a school day, freeing up more time for children to spend away from screens on outdoor collaborative play, and to cultivate interpersonal life skills.

Back-office administrative AI empowers educators to focus more on direct interactions with students. Globally, teachers spend on average 50% of their working hours on non-teaching activities32 — creating presentation slides, grading rubrics, scheduling, and aligning lesson plans with state curricula. By offloading these workflows to AI, these tools function as an assistive technology that augments, rather than replaces, the essential role of a human educator.

Finally, because of the heightened public concern around the dangers of social media and the internet, a strong market opportunity for fiduciary AI — an intermediary layer or operating system-level intervention that protects children on their devices — arises. Seeing this opportunity, investors channel resources towards founders developing AI systems that can block nude images, prevent grooming, and enforce safety guardrails before the child interacts with broader AI environments.

2033 → 2035

Building a Resilient Generation

Ultimately, by deliberately designing AI and the digital environment with these child-centric guardrails, society fosters a generation that is cognitively robust and emotionally resilient. Rather than becoming isolated or cognitively de-skilled, these children develop strong executive functioning, critical problem-solving abilities, and the interpersonal skills necessary to adapt to a rapidly changing world.

When they enter the future labour market, this cohort will be positioned “above the algorithm” — capable of thoughtfully directing advanced technology rather than being manipulated or directed by it — leading to productivity gains across the economy as the new generation becomes uniquely equipped to innovate and take leadership.

Side by side

The Two Futures Compared

The same variables, resolved two ways. The difference between the columns is not technological capability — it is design intent and where capital chooses to flow.

Variable Worst case Best case
VC / investor behaviour Speed prioritised over safety; no meaningful child rights due diligence; engagement and dwell time treated as primary success metrics — the mistakes of the social media era repeated. Asset owners recognise the market gap for AI that upholds child rights; capital directed toward back-office AI and narrow, assistive, evidence-backed tools; success metrics shifted from engagement to learning outcomes, wellbeing, and time returned to teachers.
Design approach Child safety sidelined; no input from child development experts; products optimised for addictive engagement and data extraction; responsibility offloaded to parents. Child development experts, pediatricians and educators consulted throughout; safety built in from the ground up rather than siloed into Trust & Safety; products extensively trialled before launch and iterated post-deployment.
Cognitive skills AI removes natural friction essential to development; children offload analytical thinking and writing to machines; executive functioning stunted; a two-tier educational system emerges, exacerbating inequality. Teachers given more time for high-quality in-person teaching; personalised tutors deepen reasoning rather than provide answers; tailored tools unlock accessibility; reduced screen time frees space for outdoor play.
Emotional & social development Parasocial relationships with chatbots create emotional dependency; AI-generated CSAM proliferates; displacement of real-world connection stunts interpersonal skills; higher rates of mental health issues. Increased teacher and peer interaction enhances social development; a fiduciary AI layer blocks grooming and inappropriate content before children encounter it; guardrails prevent parasocial dependency.
Ambient exposure Recommendation algorithms push youth towards extreme content; AI-generated disinformation, hate speech and misogynist material normalised; boys exposed to nihilistic subcultures in unmoderated gaming spaces. Device-level fiduciary AI filters inappropriate content; safety guardrails enforced before children interact with broader digital environments.
Societal outcome A cognitively de-skilled, socially isolated, radicalised generation; surge in graduate unemployment, democratic backsliding, political extremism; mental health crises overwhelming healthcare systems; collapsing birth and employment rates. A generation positioned “above the algorithm” — strong executive functioning, critical reasoning and interpersonal skills; productivity gains as the cohort enters the workforce equipped to direct rather than be directed by technology.
Investor consequence Reputational and legal risks materialise as lawsuits proliferate; portfolio companies face regulatory clampdown and public backlash. Products meet parental demand and public procurement standards; strong returns from B2B EdTech and protective infrastructure.

Whichever path

Takeaways for Investors

The future direction of AI’s impact is not inevitable. The decision tree remains broad and, at many critical points, is shaped by the decisions of innovators and capital allocators. There are opportunities to invest in child-safe and child rights-respecting AI:

Protective infrastructure

Startups that act as protective infrastructure for children, such as certified intermediary AI agents or device-level safety software downloaded on children’s devices. This would be strongly supported by parental and institutional demand.

Evidenced use cases

Use cases evidenced by their potential to improve child wellbeing and learning — such as teacher administrative tools, and tailored educational tools for children with learning disabilities.

Bridging the digital divide

The highest impact can be made by AI tools within low-resource settings. This includes AI that translates high-quality educational content into historically underserved languages, or systems designed to operate offline or with minimal computational resources for communities lacking reliable internet infrastructure.

Practical actions to take

  1. Bring in the experts. Encourage portfolio companies to consult with child development experts, pediatricians and educators throughout the design process.
  2. Shift KPIs away from engagement. Success metrics should be redefined around learning outcomes or child well-being metrics — competence, autonomy and relatedness.
  3. Use the frameworks that already exist. UNICEF’s LP/GP Primer on child online safety guides VC and growth equity investors on assessing the risks; UNICEF’s RITEC35 provides a framework for designing digital play for child well-being; the EdTech Evidence Evaluation Routine (EVER)33 provides an assessment framework based on four pillars of learning; and the UN Guiding Principles on Business and Human Rights34 provide standards that portfolio companies can be held accountable to.
  4. Ask for efficacy testing. New tools should undergo efficacy testing (RCTs) with learning experts and students, then be continuously tested post-deployment, re-checked, and re-approved in an iterative process.

References

Sources

All sources below are retained from the original Reframe Venture analysis.

  1. UNICEF Innocenti (2025), Guidance on AI and Children (PDF).
  2. Machidon (2025), AI & Society, Springer.
  3. American Psychological Association, Health advisory on AI and adolescent well-being (PDF).
  4. Transparency Coalition, Report finds AI chatbots grooming kids, offering drugs, lying to parents.
  5. Reuters (2023), FBI says artificial intelligence being used for sextortion and harassment.
  6. UNICEF, Artificial intelligence and child sexual abuse and exploitation.
  7. Neugnot-Cerioli et al. (2024), The Future of Child Development in the AI Era, arXiv.
  8. BBC News, Jury finds social media companies negligent.
  9. International Bar Association, Landmark US case could launch a whole wave of addiction litigation.
  10. Psychological Science in the Public Interest, Putting education in “educational” apps, SAGE.
  11. OECD (2015), Students, Computers and Learning (PDF).
  12. Haidt, The EdTech revolution has failed, After Babel.
  13. Stanford HAI, Ethics teams in tech are stymied by lack of support.
  14. European Parliament (2025), EU AI Act briefing (PDF).
  15. UNICEF Innocenti, Policy guidance on AI for children.
  16. OECD, AI Principles.
  17. Council of Europe, Framework Convention on Artificial Intelligence.
  18. OHCHR (2021), General Comment No. 25 on children’s rights in relation to the digital environment.
  19. UNICEF Australia, Social media ban explainer.
  20. UK Government, New rules to protect children online.
  21. BBC News, VPN use and workarounds after online restrictions.
  22. CSBA (2025), Administrative Burdens: AI Taskforce (PDF).
  23. NPR, Jonathan Haidt on AI chatbots and children (transcript).
  24. The Guardian (2026), Young men, extremism and the search for belonging.
  25. Reddit, r/NEET — cited in the original as a glimpse into nihilism and radicalisation spreading among young people.
  26. Tech Transparency Project, YouTube leads young gamers to videos of guns and school shootings.
  27. New York Magazine (2019), The world of American hikikomori.
  28. The New York Times (2025), The rise of the manosphere.
  29. BBC News, Incel-led violence.
  30. The Guardian (2026), Masculinity crisis brewing in UK schools.
  31. The Verge (2026), NBC News poll on public sentiment towards AI.
  32. OECD (2015), How much time do teachers spend on teaching and non-teaching activities?
  33. npj Science of Learning (2023), Applying the science of learning to EdTech evidence evaluations using the EdTech Evidence Evaluation Routine (EVER).
  34. OHCHR, UN Guiding Principles on Business and Human Rights (PDF).
  35. UNICEF, Responsible Innovation in Technology for Children (RITEC) — the original cites this via a search-engine link; substituted here for the official page.