arXiv subjects

Mathis Brossier

Publications and source records attributed to Mathis Brossier.

4 recordsLinked to original sources

arXiv2026

Opportunities of Touch-Enabled Spherical Displays to support Climate Conversations

We explore how touch-sensitive spherical displays can support climate conversations in museums and science centers. These displays enable intuitive and embodied interaction with complex climate data, and support collective exploration. However, current interaction capabilities of spherical displays are limited. Therefore, this exploratory study aims to identify potential opportunities to develop meaningful interactions and technical solutions. Through two workshops, key opportunities were identified to improve visitors' understanding and navigation of climate data, along with recommendations for technical implementation. Our results provide guidelines and aspects to consider for future research and development in this area.

cs.HC↗

arXiv2026

Piloting Planetarium Visualizations with LLMs during Live Events in Science Centers

We designed and evaluated an AI pilot in a planetarium visualization software, OpenSpace, for public shows in science centers. The piloting role is usually given to a human working in close collaboration with the guide on stage. We recruited 7 professional guides with extensive experience in giving shows to the public to study the impact of the AI-piloting on the overall experience. The AI-pilot is a conversational AI-agent listening to the guide and interpreting the verbal statements as commands to execute camera motions, change simulation time, or toggle visual assets. Our results show that, while AI pilots lack several critical skills for live shows, they could become useful as co-pilots to reduce workload of human pilots and allow multitasking. We propose research directions toward implementing visualization pilots and co-pilots in live settings.

cs.HC↗

arXiv2026

State of the Art of LLM-Enabled Interaction with Visualization

We report on a systematic, PRISMA-guided survey of research at the intersection of LLMs and visualization, with a particular focus on visio-verbal interaction -- where verbal and visual modalities converge to support data sense-making. The emergence of Large Language Models (LLMs) has introduced new paradigms for interacting with data visualizations through natural language, leading to intuitive, multimodal, and accessible interfaces. We analyze 48 papers across six dimensions: application domain, visualization task, visualization representation, interaction modality, LLM integration, and system evaluation. Our classification framework maps LLM roles across the visualization pipeline, from data querying and transformation to visualization generation, explanation, and navigation. We highlight emerging design patterns, identify gaps in accessibility and visualization reading, and discuss the limitations of current LLMs in spatial reasoning and contextual grounding. We further reflect on evaluations of combined LLM-visualization systems, highlighting how current research projects tackle this challenge and discuss current gaps in conducting meaningful evaluations of such systems. With our survey we aim to guide future research and system design in LLM-enhanced visualization, supporting broad audiences and intelligent, conversational interfaces.

cs.HC↗

arXiv2026

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

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.

cs.HC↗