We present our first stage results from deploying an LLM-augmented visualization software in a classroom setting to engage primary school children with earth-related datasets. Motivated by the growing interest in conversational AI as a means to support inquiry-based learning, we investigate children's expectations, engagement, and evaluation of a spoken LLM interface with a shared, immersive visualization system in a formal educational context. Our system integrates a speech-capable large language model with an interactive spherical display. It enables children to ask natural-language questions and receive coordinated verbal explanations and visual responses through the LLM-augmented visualization updating in real time based on spoken queries. We report on a classroom study with Swedish children aged 9-10, combining structured observation and small-group discussions to capture expectations prior to interaction, interaction patterns during facilitated sessions, and children's reflections on their encounter afterward. Our results provide empirical insights into children's initial encounters with an LLM-enabled visualization platform within a classroom setting and their expectations, interactions, and evaluations of the system. These findings inform the technology's potential for educational use and highlight important directions for future research.

翻译：我们报告了在课堂环境中部署一款大型语言模型增强可视化软件的第一阶段成果，旨在促进小学生与地球相关数据集的互动。受对话式人工智能作为探究式学习支持手段日益增长的兴趣驱动，我们研究了在正规教育背景下，儿童对具备语音交互功能的大型语言模型与共享沉浸式可视化系统结合的期望、参与度和评价。我们的系统将支持语音交互的大型语言模型与交互式球形显示屏相集成，使儿童能够提出自然语言问题，并通过基于语音查询实时更新的大型语言模型增强可视化，获得协调的语音解释与视觉反馈。我们报告了一项针对9-10岁瑞典儿童的课堂研究，结合结构化观察和小组讨论，捕捉互动前的期望、引导式课程中的交互模式，以及儿童在体验后的反思。研究结果为理解儿童在课堂环境中初次接触大型语言模型赋能可视化平台的期望、互动行为及系统评价提供了实证依据，这些发现揭示了该技术在教育应用中的潜力，并指明了未来研究的重要方向。