CONCEPT
AI Practice Framework (Developmental)
The clinical prescription for children's AI use — alternation, latency, incompleteness, protected unstructured time — extending the Berkeley workplace framework to the developmental context.
The AI Practice framework, originally articulated by Berkeley researchers Xingqi Maggie Ye and Aruna Ranganathan for the AI-augmented workplace, translates into the developmental context as a set of concrete practices for families, educators, and institutions. The four core practices are alternation (AI-assisted work followed by unassisted work, in structured sequence), latency introduction (tools that deliberately delay responses to reintroduce the waiting that natural interaction provides), structured incompleteness (partial scaffolding that preserves the child's role as cognitive agent), and protected unstructured time (periods during which the child has access to no AI, no screen, no structured activity, and boredom is not just permitted but expected). The framework is not a prohibition but a design for cognitive environments that preserve the developmental inputs AI naturally eliminates.
In The You On AI Field Guide
Alternation is the most immediately implementable practice. A child uses AI for thirty minutes to explore a topic, then turns it off and writes from her own understanding for an hour. The AI expanded her