Before Kids Learn to Think, They’re Learning to Prompt

Almost a year ago, on the way to soccer practice, my six-year-old asked a question that felt both innocent and quietly inevitable: “Dad, where does ChatGPT live?” Like many children encountering generative A.I. for the first time, he was trying to place it in the world by giving it a location, a biography and a set of boundaries. That instinct matters. Long before children understand how A.I. systems work, they relate to them socially: asking questions, seeking explanations and attributing knowledge, agency and even personality to these tools that respond fluently and with confidence. 

For parents, educators and policymakers, the question is not whether children will interact with generative A.I.—they already are—but rather how those interactions shape formative minds when reasoning, writing and judgment are still being formed. 

My concerns had been simmering long before my son’s query brought them to a boil: What effect would these new tools have on the mind of an impressionable six-year-old? What changes, mundane and grand, would they give rise to in a world he was only beginning to understand? And, finally, what responsibilities did I have as his father to protect and guide him through the very technology that I helped advance in my role as the CEO of an A.I. company?

Parental unease 

The unease around generative A.I.—and its impact on children—is not unique to technology founders or practitioners. Parents, educators and policymakers have all converged around shared concerns about what children should be taught about these systems and what limits, if any, should govern their use. 

Such concerns have become more pronounced as major platforms like OpenAI have moved generative A.I. closer to everyday use. Over the past year, leading models have gained stronger memory, conversational richness and enhanced reasoning, which increase utility for adults while deepening attachment risks for children. 

Every generation, it must be said, laments the negative effects of emerging technologies on younger minds. Radio, calculators, television and even the internet were all once framed as problematic novelties before becoming everyday tools. Generative A.I. differs in a critical respect insofar as it can supplant the act of thinking itself. For children still learning how to reason, write and wrestle with ideas, the concern is clear: what happens when cognitive effort is outsourced before it has been fully developed?

Before grappling with this topic, it is useful to begin with a broader and better-documented question: what does existing evidence tell us about how generative A.I. is already shaping adult cognition? 

Generative A.I. and adult cognition 

The effects of generative A.I. on professional knowledge workers have been thoroughly examined, including a widely cited study by Microsoft Research. Among its central findings is that while generative tools can increase efficiency on particular tasks, their unchecked use can reduce critical thinking. 

The authors describe a phenomenon they term mechanized convergence: users relying on generative systems tend to produce narrower, less diverse outputs than those working independently. A similar pattern was reported in Nature, where researchers found that while A.I. tools expanded the scale of scientific work, they often contracted focus and originality. 

According to the Microsoft study, automating everyday tasks “deprives the user of the routine opportunities to practice their judgement and strengthen their cognitive musculature”. Each time a task—drafting an email, summarizing an article, polishing a LinkedIn post—is outsourced, a small but cumulative cost accrues: the loss of cognitive practice. 

But isn’t the cost worth it, given the productivity benefits? As the refrain goes: jobs will not be replaced by A.I., but by people who can wield A.I. effectively. If this is the case, why shouldn’t such tools be used with impunity? What this framing overlooks  are the cognitive demands required by meaningful human-machine collaboration: strong writing skills to craft precise prompts, and critical thinking abilities to evaluate outputs, identify flaws and iterate thoughtfully. Ironically, these are precisely the skills that erode when generative tools are used without restraint. 

For adults, this paradox raises a key question: how can generative A.I. be used to preserve and amplify critical thinking rather than diminish it? For children, who are only beginning to develop these capacities, the stakes are even higher.  

The use of generative A.I. by young children 

For the purposes of this analysis, young children are defined as those between the ages of four and 12. Precise data on generative A.I. use for this cohort remains limited, in part because many platforms restrict formal access for younger users and usage often occurs indirectly through parents, siblings or educational tools. 

Still, recent research offers a useful window into emerging patterns. A 2025 report by the Alan Turing Institute found that 16 percent of children aged eight to 12 in the U.K. reported using generative A.I. tools daily, with nearly half using them at least weekly. A U.S. study by Common Sense Media reported lower direct use among younger children—six percent of those aged five to eight—but significantly higher exposure in educational contexts, where nearly one-third encountered A.I.-enabled learning tools. 

Taken together, these findings suggest that while daily chatbot use remains relatively uncommon in early childhood, interaction with generative A.I. increases markedly in late primary and pre-teen years. As A.I. tools become embedded across educational systems, productivity platforms and consumer devices, this exposure is likely to grow structurally rather than episodically. 

In practice, the use of generative A.I. in early childhood tends to cluster around four overlapping activities: information seeking, creative play, conversational interaction and homework support, including summarization, drafting and step-by-step assistance.

While many parents report increased engagement and curiosity, concern is widespread. In the U.K. study, more than two-thirds of parents expressed unease, citing risks such as misinformation, inappropriate content and overreliance on automated assistance during formative stages of development. 

Generative A.I. and child cognition 

As with adults, the evidence suggests that generative A.I. can either support or undermine learning depending on how it is used. Across disciplines and age groups, a growing body of research points to a consistent conclusion: A.I. tools enhance learning when they prompt explanation, evaluation and reflection, but undermine it when they replace thinking, practice and persistence. 

The mechanisms behind the benefits and risks of the technology are instructive:

• Cognitive offloading: children may outsource memory, planning or reasoning to the model, reducing opportunities to build and bolster foundational skills.
• Automation bias: students may accept confident-sounding answers without verification, especially when tools are anthropomorphized.
• Scaffolding and formative feedback: when prompts require students to justify, compare or explain, generative A.I. can support higher-order cognition and deepen comprehension.
• Reduced productive struggle: when models supply completed solutions, children lose opportunities to wrestle with problems, make errors and build fluency through effort.

The need for scaffolding and developmentally appropriate tools echoes the findings from older student populations. A field experiment in high school mathematics, for example, found that A.I. tutors lacking strong pedagogical guardrails reduced learning outcomes despite improving short-term productivity. An MIT study on adult essay writing similarly reported that 83 percent of participants who used generative A.I. could not recall a single quote from their own essay, whereas 89 percent of participants who did not use A.I. assistance were able to do so.

Such findings illustrate the pronounced risks of generative tools in early childhood, when critical thinking is built through repeated cycles of asking questions, checking evidence, comparing alternatives, explaining reasoning and reflecting on errors. When A.I. is used as a shortcut—when it becomes a substitute for thinking rather than a tool to provoke it—it can crowd out the very processes that learning depends on. 

For this reason, the consistent guidance is that generative A.I. should be positioned not as a solution generator but as a reasoning catalyst. That is, a tool that generates options, counterexamples or partial explanations for children to interrogate. This reframe is especially important in elementary education, where reading, writing, numeracy and executive function are still developing. 

Policy implications 

Not surprisingly, these concerns have prompted educational and governmental bodies to formalize clear policies on the use of generative A.I. in learning environments. In recent years, guidance has emerged from authorities in England, California, Massachusetts, the government of New South Wales in Australia, as well as multilateral organizations such as UNICEF and UNESCO. While these bodies differ in emphasis, their recommendations converge on a shared concern: generative A.I. must be used in a manner that protects formative cognition while supporting meaningful learning.

In concert, these frameworks articulate some key principles that educators and parents would do well to keep in mind:

• Use bounded tools for younger children: prioritize managed tools with strong filtering, logging, and privacy controls, and avoid open, general-purpose chatbots with weak content moderation or terms that restrict under-13 use.
• Design prompts that force reasoning: require students to explain outputs, identify errors, compare sources and justify conclusions.
• Prioritize process artifacts: emphasize drafts, planning notes, checklists and reflections (e.g., “What did you change after checking?”) to ensure visible thinking remains central to the learning process.
• Teach verification literacy early: introduce age-appropriate instruction on hallucinations, confidence versus correctness, source checking and bias; establish norms such as “A.I. is a starting point, not an authority.”
• Communicate with families: make transparent which tools are approved, the data they collect and the expectations that apply at home (e.g., shared use in common areas, time limits and prohibitions on private, unsupervised chats).
• Protect time for non-digital learning: ensure that reading, handwriting, mental math and unstructured play remain substantial and robust, such that generative A.I. supplements rather than displaces foundational skill-building.

These policy responses reflect a growing recognition within educational communities: the question is no longer whether generative A.I. can assist with learning, but rather how it can be integrated without eroding the conditions that learning depends upon.

Out-of-the-box thinking 

Much of the current discourse has been reactive, emphasizing mitigation, guardrails, and the assimilation of generative A.I. into existing pedagogical structures. While valid, a parallel question deserves attention: how might education be reimagined with generative A.I., rather than merely defended against it? 

To this end, a more forward-looking perspective is offered in a recent whitepaper, AI and the Future of Pedagogy. While expanding on the cautionary points above, it also explores how generative A.I. might enable new forms of learning that were previously impractical. A single example is instructive. 

Consider the topic of student testing. Instead of relying on static, fact-based assessments, the whitepaper imagines an adaptive, Socratic evaluation environment powered by generative models. In this paradigm, the assessment becomes a guided conversation that probes student comprehension in a variety of directions, and shifts the underlying question from “What do you know?” to “How do you think?” Competence is measured not by recall, but by the ability to apply concepts in novel contexts, articulate reasoning and connect disparate ideas; capacities that align more closely with the cognitive skills education ultimately seeks to cultivate.

An educational system that fuses thoughtful experimentation with the wisdom of its practitioners is a credible cause for optimism. 

A look to the future 

Futurist Rahim Hirji observes that “by 2030, the question will no longer be whether automation replaces jobs…[but] what humans choose to become once intelligence is abundant.” In this framing, the long-term stakes of technology will depend less on the mastery of specific systems and more on qualities that remain eminently human: curiosity, agency, empathy, integrity and the capacity to ask better questions rather than simply retrieve better answers.

In their fourth Economic Index Report, Anthropic, creator of the leading model Claude, developed a metric called “human education years,” an estimate of the number of years of formal schooling an individual requires to understand a user’s prompts and the model’s responses. 

A key finding is that the sophistication of the model’s answer closely mirrors the complexity and nuance of the user’s prompt. In other words, A.I. does not nullify the fundamentals; it accentuates them. Core skills centered on reading, clear writing and structured reasoning are not redundant in the age of A.I., but prerequisites for extracting its value. These capacities must be cultivated deliberately, particularly in childhood, as generative systems continue to grow in capability and influence. 

Returning to my son’s original inquiry, how might a parent respond to their child’s question of “Where does ChatGPT live”? One might encourage them to ask the model that very question and then probe it with relentless curiosity—how, when and why. But, in the spirit of this analysis, the key lies in the follow-up thereafter, one that conscientious parents of generations past knew well: lively discussion, tender mentorship and informed debate. 

The responsibility now falls to adults to model and foster a point that will become increasingly important in this age of technical adolescence: A.I. doesn’t replace thinking—it rewards it.