为儿童设计更智能的对话助手，用结构化引导提升学习体验。
Designing Smarter Conversational Agents for Kids: Lessons from Cognitive Work and Means-Ends Analyses
- 通过认知工作分析，梳理儿童使用对话助手的三类功能与互动模式。
- 结构化提示使对话在可读性、问题深度和多样性上均显著优于无结构基线。
- 适合教育科技开发者与儿童数字产品设计师参考。
本文通过两项研究探讨巴西9至11岁儿童如何使用对话助手（CAs）完成学业、探索知识和娱乐，并研究结构化支架如何增强交互体验。研究一为期七周，涵盖23名参与者（儿童、家长、教师），结合访谈、观察与认知工作分析（CWA），揭示了儿童的信息处理流程、重要他人角色、功能用途、情境目标与互动模式，据此提炼出三类CA功能：学业、探索、娱乐，并设计出模拟亲子支持的“配方”式支架。研究二利用GPT-4o-mini对1,200次模拟儿童-助手对话进行测试，对比基于结构化提示的“配方”与无结构基线。量化评估显示，配方方法在可读性、问题数量/深度/多样性及连贯性方面均有提升。基于成果，提出三项设计建议：构建有支架的对话树、为儿童设立专属档案以实现个性化上下文、由监护人筛选内容。本研究首次将CWA应用于巴西儿童，建立了儿童-对话助手信息流的实证框架，并提出一种基于大模型的结构化提示“配方”，助力有效、支架式学习。
原文摘要 · Abstract (English)
This paper 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 ``recipe'' scaffolds mirroring parent-child support. In Study 2, we prompted GPT-4o-mini on 1,200 simulated child-CA exchanges, comparing conversation-tree recipes based on structured-prompting to an unstructured baseline. Quantitative evaluation of readability, question count/depth/diversity, and coherence revealed gains for the recipe 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 ``recipe'' (i.e., structured-prompting) for effective, scaffolded learning.
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