Hey Dashboard!: Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding
Abstract
Visualization dashboards are regularly used for data exploration and analysis, but their complex interactions and interlinked views often require time-consuming onboarding sessions from dashboard authors. Preparing these onboarding materials is labor-intensive and requires manual updates when dashboards change. Recent advances in multimodal interaction powered by large language models (LLMs) provide ways to support self-guided onboarding. We present DIANA (Dashboard Interactive Assistant for Navigation and Analysis), a multimodal dashboard assistant that helps users for navigation and guided analysis through chat, audio, and mouse-based interactions. Users can choose any interaction modality or a combination of them to onboard themselves on the dashboard. Each modality highlights relevant dashboard features to support user orientation. Unlike typical LLM systems that rely solely on text-based chat, DIANA combines multiple modalities to provide explanations directly in the dashboard interface. We conducted a qualitative user study to understand the use of different modalities for different types of onboarding tasks and their complexities.
Keywords
Cite
@article{arxiv.2510.12386,
title = {Hey Dashboard!: Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding},
author = {Vaishali Dhanoa and Gabriela Molina León and Eve Hoggan and Eduard Gröller and Marc Streit and Niklas Elmqvist},
journal= {arXiv preprint arXiv:2510.12386},
year = {2026}
}