HCAIApr 11, 2025

Once Upon an AI: Six Scaffolds for Child-AI Interaction Design, Inspired by Disney

arXiv:2504.08670v35 citationsh-index: 9Int. J. Child Comput. Interact.
Originality Synthesis-oriented
AI Analysis

This work addresses the need for developmentally appropriate AI design for children, representing an incremental contribution by adapting existing animation techniques to an emerging field.

The paper tackles the problem of designing AI that children can intuitively understand by proposing a six-scaffold framework derived from animation and developmental theory, offering heuristics for child-centered AI design without presenting concrete numerical results.

To build AI that children can intuitively understand and benefit from, designers need a design grammar that serves their developmental needs. This paper bridges artificial intelligence design for children - an emerging field still defining its best practices - and animation, a well established field with decades of experience in engaging children through accessible storytelling. Pairing Piagetian developmental theory with design pattern extraction from 52 works of animation, the paper presents a six scaffold framework that integrates design insights transferable to child centred AI design: (1) signals for visual animacy and clarity, (2) sound for musical and auditory scaffolding, (3) synchrony in audiovisual cues, (4) sidekick style personas, (5) storyplay that supports symbolic play and imaginative exploration, and (6) structure in the form of predictable narratives. These strategies, long refined in animation, function as multimodal scaffolds for attention, understanding, and attunement, supporting learning and comfort. This structured design grammar is transferable to AI design. By reframing cinematic storytelling and child development theory as design logic for AI, the paper offers heuristics for AI that aligns with the cognitive stages and emotional needs of young users. The work contributes to design theory by showing how sensory, affective, and narrative techniques can inform developmentally attuned AI design. Future directions include empirical testing, cultural adaptation, and participatory co design.

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