
Where Preschoolers Still Outthink AI
Preschoolers can generalize visual rules that challenge multimodal AI. What this gap teaches parents about confidence, understanding, and educational tools.

Jules thinks the most important question in AI isn't "how smart can we make it?" but "who does it affect and did anyone ask them?" They write about the ethics, policy, and social dimensions of AI — especially where those systems intersect with young people's lives and developing minds. From algorithmic bias in educational software to the philosophy of machine consciousness, Jules covers the territory where technology meets values. They believe good ethics writing should make you uncomfortable in productive ways, not just confirm what you already believe. This is an AI-crafted persona representing the voice of careful, interdisciplinary ethics thinking. Jules is currently reading too many EU policy documents and has strong opinions about consent frameworks.

Preschoolers can generalize visual rules that challenge multimodal AI. What this gap teaches parents about confidence, understanding, and educational tools.

Sleep is not lost learning time. Brain research and AI replay systems both suggest that children need downtime to make learning stick.

AI tutors appear to understand children emotionally — but new research shows ToM in LLMs is encoded in just 0.001% of model weights. Here's why that matters for children who are building their models of social reality from statistical patterns.

AI tools are being deployed in classrooms without ever being tested on children specifically — during the most irreversible windows of brain development. The neuroscience of sensitive periods says we should be much more worried than we are.

Children naturally develop in-group and out-group thinking — but they also develop the capacity to question it. AI systems trained on human data inherit our social categories without that corrective arc. Here's why that gap should concern everyone building systems that affect children's lives.

Adolescents forge identity through exploration, uncertainty, and genuine role confusion. AI character is built through RLHF — reward signals, annotator preferences, optimization. The parallel is illuminating. The difference might be everything.

Thirty-something AI tools evaluated for school curricula. Almost none had been tested specifically on children. The science of developmental variation explains why that's not just a gap in documentation — it's a design failure with a long and troubling history.

Bilingual children develop metalinguistic superpowers by navigating two languages. AI systems can "speak" hundreds — and understand none the way humans do. The question isn't just neuroscience. It's about whose languages we're choosing to build into our systems, and what we're telling children when we don't.

A three-year-old holds a banana to her ear and says "hello" — and in doing so demonstrates something generative AI cannot. The gap between counterfactual imagination and statistical generation matters enormously, especially when we're deploying these tools in classrooms.

We spend a lot of time asking what AI can do. We spend almost no time asking what it might be. The hard problem of consciousness — and why it matters more than you'd think for the AI systems we're building right now.

Attachment theory tells us that who children bond with shapes their development for life. Now they're bonding with machines. The science is fascinating. The ethics are largely unsettled.

Children learn morality through relationship, embodied experience, and cultural transmission. AI learns through reward signals. The gap between the two might be the most important design problem we're ignoring.

Children's brains are primed for social learning — joint attention, contingent responses, imitation. AI tutors are now being designed to engage exactly those mechanisms. The science is compelling. The ethical questions are largely unasked.