Critical thinking without hope is cynicism.
Hope without critical thinking is naivete.
—Maria Popova
Genuine understanding is not received;
it is achieved.
—Jason Baehr
As more and more AI programs keep escaping their cages[i]—don’t you love the euphemism, “sandboxes?”—it was time to examine the latest in AI and its potential impacts on teaching and learning, and for once, I actually had two ideas brought to my attention that made me go “Hmm!” with my positive, “that’s interesting!” vocal tone rather than my head-shaking “Hmm!” with my “we’re f-ing doomed!” vocal tone. But there was still some “f-ing doomed” material in the recent news; so let’s get that out of the way first.
My “favorite” from the past month is the news of how AI is infiltrating childhood at an ever earlier age. Greg Rosalsky of KQED’s Mindshift has reported on AI’s move into the toy industry, with interactive dolls and fuzzy robots intentionally designed to befriend children as young as two-years-old, and as he points out, “companies are racing to embed AI into toys, nurseries, classrooms, and eventually robots that live alongside families,” with “recommendation algorithms [that already] curate what many kids watch and listen to” in their bedrooms and nurseries. Indeed, the hottest trend among Millennial and Gen Z parents is apparently one of two baby-monitoring systems (Nanit and Owlet) where (as described by one user in the NYT):
A sleek white camera, bolted to the wall, points straight down at her body. High-definition footage is beamed to a server, where machine-learning algorithms log her movements and the precise moments her eyes open and close. The data are distilled into charts and insights that my husband and I review in the Nanit app: a daily “sleep efficiency” score, from zero to 100; minutes until sleep onset; the number of times we were needed over the course of a night [and while] it is the Eye of Sauron…it also knows when we enter our daughter’s room in the morning and joy hits her face—and it sends us intelligently edited montages of these moments.
Moreover, so enthralled was the author of these words by this new use of AI that “when I became pregnant with a second child in 2025, my husband and I did not even have a conversation about buying another Nanit camera. We had it waiting the day she came home from the hospital.”
Hmm. The authoritarian’s “wet dream:” an entire generation socialized and conditioned to accept 24/7 video monitoring in every location they ever inhabit, including the bedroom (a pornographer’s “wet dream”). If we think we have no privacy now….
However, what truly disturbs me even more than baby cam on steroids is the impact the adoption of these technologies by so many parents will have on the most important stage of brain development. As Dr. Dana Suskind, a pediatric surgeon and neuroscientist reminds us:
At a glance, a child talking to an AI companion looks like a child participating in conversation. But essential, quiet interactions have been stripped away. Imperfections, delays, and the need to apologize or rephrase something may seem like flaws in human communication, but they are, in fact, crucial developmental building blocks. The small cycle of rupture and repair that occurs following a misunderstanding, played out thousands of times over a childhood, is how resilience, flexibility, and emotional regulation get built. It’s how children develop the most important relational skills: trusting that relationships can survive conflict, and staying connected with others through discomfort. Remove the friction from childhood, and the social-emotional system never gets what it needs to properly develop. The neural architecture that was supposed to be built through imperfect, sometimes-frustrating human interaction may never fully form.
Or to put it another way (Suskind argues), since social interaction is to the developing neural networks what basic nutrition is to the body, AI is the equivalent of ultra-processed foods—i.e. junk food—for the brain. As she puts it:
Because AI companions have been engineered to remove every imperfection—the misunderstanding, the delay, the repair—a child’s social-emotional system never has to work. Ultra-processed foods stripped the fiber from diets, and engagement-optimized AI might strip the friction from childhood. The developing mind, like the gut, needs resistance to grow. Friction isn’t an obstacle to development; it is development (my emphasis).
Moreover, since we know that social-emotional intelligence is absolutely critical to the proper functioning of the rest of our cognitive systems, anything that messes with the proper development of those EQ networks in the brain sets a brain up for a life-time of disadvantages that Suskind pointedly emphasizes “can’t be engineered or recovered later.”
And the very people building all this technology know it. Suskind reminds us that “high-income families began pivoting away from ultra-processed foods as soon as the potential harms of such foods became apparent” that “an identical pattern is emerging with AI. Many of the engineers and executives building these products are going screen-free at home and doubling down on unstructured play.” Thus, “if the food parallel holds, then engagement-optimized AI will become the new Dollar Menu, concentrating in the lives of the children who can least afford its developmental cost,” and we will risk “the possibility that we’ll allow human connection to become the new organic—cherished and protected by the wealthy, and out of reach for everyone else.”
Again, more authoritarian “wet-dream:” a world full of cameras with nothing but sheep to watch. Stalin, Hitler, and Pol Pot could only have fantasized, and I sometimes wonder if Donald Trump actually does given his administration’s attacks on education and its heavy federal research investment in all things AI.
However, as shared earlier, not everything I read about AI this past month made me want to go “ugh,” and one idea that I found particularly interesting came from fellow educator, Maureen Gassert Lamb, writing for the primary blog of the National Association of Independent Schools. In her essay, Lamb shares an exchange she had with one of her students in which the student had asked “If I can use AI to do this assignment, what does that actually mean about the assignment?” To which Lamb shares that she had responded (in good Socratic fashion) by asking what the student thought that meant, with the child answering “I think it means maybe the assignment isn’t really measuring what I know. Or how I think.” Together, Lamb reports, she and the student came to the mutual conclusion that “if assessments are doing what they’re supposed to do,” then what the use of AI is showing in their particular situation is that “it’s not creating the problem. It’s just making it obvious.”
Out of the proverbial mouths of babes. Maybe, Lamb suggests, “AI is not the enemy. Instead, it is a mirror, showing us where our assessments or ideas about learning may be falling short,” concluding that:
When a student can produce work that looks complete without explaining their choices, questions, revisions, or reasoning that shaped their work, AI has not necessarily created the problem. Instead, AI may simply be revealing that we have valued completion more than evidence of understanding.
Learning, she reminds us, is a journey, not a destination, and maybe the most important thing AI’s arrival has done is to force all of us in education to remember this great truth and to revisit what we have turned modern schooling into instead.
Because even as far back as 1983’s famous A Nation at Risk, the focus of educational policy in this country has been nearly exclusively on outcomes, not how we achieve them, and the litany is long: No Child Left Behind, Common Core, Every Student Succeeds. Even in a discipline as process-oriented as science, AAAS’s Project 2061, NRC’s National Science Education Standards, and their current descendant, NGSS, the focus is on outcomes, and what all these so-called reform efforts make abundantly clear is that the “what” in schools is considered far more important than the “how.” We are positively obsessed in this culture with a product’s image, and our efforts at educating our children simply (and clearly) reflect this fact.
Even the alleged recent reduction in scoring standards on AP exams—producing scores that are “particularly incongruous with the results of virtually all other standardized testing from the last half-decade, which have shown significant drops in academic achievement”—is simply the College Board bowing to the current grade-inflation pressures experienced by all educational institutions. After all, both students and parents alike have come to expect to “look good” when it comes to any comparison with their peers.
Yet, anyone who has ever learned to build, make, or craft anything of any kind discovers quite early in that building, making, crafting journey that the qualities of any product—whether essay, sculpture, baked-good, automobile, knowledge…any product—the final quality depends totally and foundationally on the quality of the building, making, crafting process that went into it. Superior output only comes from superior input, and so what Lamb is suggesting the eruption of AI is compelling those of us in schools to revisit is “finding creative, practical ways to make the learning journey, and not only the final product, more visible.” Happily, she reports seeing it beginning to happen in her school, and I can share that I just spent an entire summer working on a grant with my fellow biology teachers to do the same throughout our entire 9th grade curriculum. So, there is hope.
Hope, though, without critical thinking—as Maria Popova reminds us—is naivete, and one of the things AI is also forcing us to rethink is how we use our leisure time, bringing me to the other story about AI which I read recently that provoked thoughtful interest rather than despondency. Fellow educator, Mike Chapman—also writing for NAIS’s blog—is an outdoor ed colleague who has observed that when presented with situations involvingt unstructured time, “with no block schedules, bells, or electronics in sight,” his students have had very mixed reactions. As he describes it:
To a precious few, this was a wonderful, fleeting opportunity for personal art, music, reading, or games, or a moment to sit in a circle and talk, relax and unwind, or lay in the shade in collective solitude. But for most, it was paralyzing—an anxious state of idleness, an inactive tedium without digital distractions to fill the void—a symptom of having been able to avoid this exact condition for the majority of their lives.
Yet rather than despair at this fact, Chapman recognizes that “perhaps this paralysis in the face of time is an indication of exactly the sort of education our students desperately need,” and he goes on to make the case that with the advent of AI, it is likely that our students will find themselves as adults in a world where there is less labor of all kinds (both on the job and at home) and therefore that they will have significantly more unstructured time for them to find ways to fill. The danger of AI doing this for them, Chapman argues, is obvious (and I agree), and thus:
If work demands less human input in the future, what, then, should education prepare students for? How can schools help them choose pursuits that matter not because they are necessary or productive, but because they are inherently worthwhile? Education, then, must do more than prepare students for employment; it must also prepare them for meaningful leisure.
How we as educators will do that, I have no idea yet (and I suspect neither does Chapman). But I find it a worthy notion to explore further, and I invite my fellow teachers to join me in giving it some thought and future praxis to see what we might pull off. What I do know, though, is that we will not be helping our students find meaningful leisurely pursuits by bolting cameras to every wall from the day they are born to watch (and therefore consequently guide) their every move. Leisure, after all, implies at least a minimal degree of unrestricted freedom.
[i] In fact, these escapes have started to happen so often now that they are not even worth a formal reference or citation anymore; it would be like reporting as news “the sun came up today.”
References
Chapman, M. (Aug. 11, 2026) Learning for Leisure. NAIS. https://www.nais.org/resource-center/independent-ideas/august-2026/learning-for-leisure?utm_medium=email&utm_campaign=NAIS%20Bulletin%2081226&utm_content=NAIS%20Bulletin%2081226+CID_f5bb87812a0f6c4ea95235d334370639&utm_source=cm&utm_term=In%20this%20weeks%20new%20blog%20post.
Lamb, M. G. (July 21, 2026) When AI Holds Up the Mirror. NAIS. https://www.nais.org/resource-center/independent-ideas/july-2026/when-ai-holds-up-the-mirror?utm_medium=email&utm_campaign=NAIS%20Bulletin%2072226&utm_content=NAIS%20Bulletin%2072226+CID_2ef40ad9fda4d759d922d66596233c92&utm_source=cm&utm_term=In%20this%20weeks%20new%20blog%20post.
Maheshwari, S. (Aug. 2, 2026) Aw, It’s Baby’s First A.I. Surveillance System. The New York Times. https://www.nytimes.com/2026/08/02/business/smart-baby-monitors-nanit-owlet.html.
Mahnken, K. (July 27, 2026) Settling Scores: Experts Debate Why AP Performance Has Soared. The 74. https://www.the74million.org/article/settling-scores-experts-debate-why-ap-performance-has-soared/.
Rosalsky, G. (July 16, 2026) “The Trojan Teddy Bear”: The Promise and Peril of Childhood in the Age of AI. KQED Mind/Shift. https://www.kqed.org/mindshift/66480/the-trojan-teddy-bear-the-promise-and-peril-of-childhood-in-the-age-of-ai.
Suskind, D. (Aug. 7, 2026) A Chatbot-Free Childhood Will Be a Status Symbol. The Atlantic. https://www.theatlantic.com/technology/2026/08/ultra-processed-childhood-ai/688217/?utm_source .
Light a Candle 