Children's Expectations, Engagement, and Evaluation of an LLM-enabled Spherical Visualization Platform in the Classroom

2026-01-16

AI Summary

This study investigates the expectations, engagement, and user experiences of 9–10-year-old elementary students interacting with an integrated system that combines a speech-driven large language model (LLM) and a spherical immersive visualization platform to support inquiry-based learning in Earth science. The system uniquely merges voice-interactive LLM capabilities with a shared spherical display, enabling students to pose questions in natural language and receive real-time spoken explanations alongside dynamic visual feedback. Through classroom observations and group discussions, the research uncovers cognitive and behavioral patterns in children’s interactions with generative AI, offering empirical insights and an innovative design paradigm for AI-enhanced visualization tools in educational contexts.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Interaction Techniques and Devices

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• children
• LLM-enabled visualization
• classroom
• engagement
• expectations

Innovation

• LLM-augmented visualization
• conversational AI
• spherical display
• inquiry-based learning
• spoken language interface