7 articles
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.
Jules Okafor·
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.
Jules Okafor·
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.
Jules Okafor·
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.
Jules Okafor·
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.
Jules Okafor·
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.
Jules Okafor·
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.
Jules Okafor·