🤖 AI Summary
This study addresses the narrative incoherence exhibited by children with language impairments, whose storytelling frequently omits essential details and logical relations. To mitigate this, we introduce the concept of visualizing narrative gaps and construct a design space for real-time detection and animation generation spanning eight narrative dimensions. The system comprises twenty parametric animations grounded in classical animation principles, dynamically matched through voice-scenario discrepancy detection and historical context algorithms to provide visual scaffolding rather than verbal prompting. This work demonstrates the efficacy of non-verbal intervention: adults comprehended most animations without instruction, while children successfully filled 46% of narrative gaps with visual assistance, representing a substantial improvement over the unassisted baseline of 13%.
📝 Abstract
Illustrated scenes are used to prompt children's stories, yet children with language difficulties often omit the details, actions, and relationships that make a story coherent. We introduce Tellimation, which animates scene elements a child has omitted or misdescribed, drawing attention to a narrative gap without saying what to tell. Its design space covers eight kinds of gaps (who is in the scene, how many, what they are like, what they are doing, where, when, how they relate, and what lies beyond the picture, such as speech and thoughts), instantiated through 20 parameterized animations grounded in classical animation principles. A real-time pipeline detects discrepancies between utterance and scene, then selects and parameterizes an animation from the scene's narrative potential and the child's history. Adults interpreted most animations without instruction (N=120); children (N=12) resolved 46% of the gaps the system identified with animations, against 13% without.