Designing Smarter Conversational Agents for Kids: Lessons from Cognitive Work and Means-Ends Analyses
Vanessa Figueiredo · ACM Transactions on Computer-Human Interaction · 2025
This article presents two studies on how Brazilian children (ages 9–11) use conversational agents (CAs) for schoolwork, discovery, and entertainment, and how structured scaffolds can enhance these interactions. In Study 1, a seven-week online investigation with 23 participants (children, parents, teachers) employed interviews, observations, and Cognitive Work Analysis to map children’s information-processing flows, the role of more knowledgeable others, functional uses, contextual goals, and interaction patterns to inform conversation-tree design. We identified three CA functions—School, Discovery, Entertainment—and derived a scaffold framework mirroring parent–child support. In Study 2, we prompted GPT-4o-mini on 1,200 simulated child–CA exchanges, comparing conversation-tree frameworks based on structured-prompting to an unstructured baseline. Quantitative evaluation of readability, question count/depth/diversity, and coherence revealed gains for the framework approach. Building on these findings, we offer design recommendations: scaffolded conversation trees, child-dedicated profiles for personalized context, and caregiver-curated content. Our contributions include the first CWA application with Brazilian children, an empirical framework of child–CA information flows, and an LLM-scaffolding framework (i.e., structured-prompting) for effective, scaffolded learning.