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<p>What would it mean to design AI not for the average user, but for the child whose fingers still miss the screen, who forgets the instructions halfway through, and who greets the voice in the box as a friend? This paper proposes Developmentally Aligned Design (DAD) as a practical and ethical framework for building AI systems that meet children where they are—cognitively, socially, and emotionally. Building on the long-standing principle of developmentally appropriate practice in early childhood education, it theorises four principles of developmentally-aligned design: (1) <i>perceptual fit</i> (e.g., anonymised phoneme‑error tuning in early‑reading apps that respects toddlers’ speech‑production limits) (2) <i>cognitive scaffolding</i> (e.g. Zone‑of‑Proximal‑Development (ZPD) progressions that govern when a tutoring agent introduces harder tasks) (3) <i>interface simplicit</i>y (e.g., storybook apps that cap menu depth and visual clutter to match preschoolers’ working‑memory span) and (4) <i>relational integrity</i> (e.g., conversational agents that introduce themselves with a developmentally clear disclaimer—“I’m a computer helper, not a real friend”). Through illustrative examples, it demonstrates how developmental science can serve as a validation layer on AI dataset curation, model fine‑tuning, and user experience choices. Adopting Developmentally Aligned Design can therefore sensitise AI systems to the distinct perceptual, cognitive, and socio‑emotional needs of young children; shift the responsibility of “proof of safety” from parents and early‑years practitioners to AI developers and vendors; and help the science of child development become a core intellectual engine of next‑generation AI innovation.</p>

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Developmentally aligned AI: a framework for translating the science of child development into AI design

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月6日

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  • Nomisha Kurian

摘要

What would it mean to design AI not for the average user, but for the child whose fingers still miss the screen, who forgets the instructions halfway through, and who greets the voice in the box as a friend? This paper proposes Developmentally Aligned Design (DAD) as a practical and ethical framework for building AI systems that meet children where they are—cognitively, socially, and emotionally. Building on the long-standing principle of developmentally appropriate practice in early childhood education, it theorises four principles of developmentally-aligned design: (1) perceptual fit (e.g., anonymised phoneme‑error tuning in early‑reading apps that respects toddlers’ speech‑production limits) (2) cognitive scaffolding (e.g. Zone‑of‑Proximal‑Development (ZPD) progressions that govern when a tutoring agent introduces harder tasks) (3) interface simplicity (e.g., storybook apps that cap menu depth and visual clutter to match preschoolers’ working‑memory span) and (4) relational integrity (e.g., conversational agents that introduce themselves with a developmentally clear disclaimer—“I’m a computer helper, not a real friend”). Through illustrative examples, it demonstrates how developmental science can serve as a validation layer on AI dataset curation, model fine‑tuning, and user experience choices. Adopting Developmentally Aligned Design can therefore sensitise AI systems to the distinct perceptual, cognitive, and socio‑emotional needs of young children; shift the responsibility of “proof of safety” from parents and early‑years practitioners to AI developers and vendors; and help the science of child development become a core intellectual engine of next‑generation AI innovation.