"Hey Dashboard!": Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding using Large Language Models

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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 comparative qualitative user study to understand the use of different modalities for different types of onboarding tasks and their complexities.
Funding Information
  • Villum Fonden (VL-54492)
  • BMIMI, BMWET, Tyrol, Vorarlberg and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies managed by FFG (911654)

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