# Hey Dashboard!: Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding

arXiv: https://arxiv.org/abs/2510.12386 · HTML: https://mlsanity.com/paper/2510.12386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards](https://mlsanity.com/paper/2606.13731/) (2026)

## Abstract (arXiv)

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.