We designed and implemented InvestChat, a multimodal tablet-based application that supports stock market exploration with multiple coordinated views and an LLM-powered chat. We evaluated the application with 12 novice investors. Our findings suggest that combining natural language, touch, and pen input during stock market exploration facilitates user engagement. Participants leveraged the modalities in complementary ways, enjoying the freedom of choice and finding natural language most effective.

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.

Domain experts possess tacit knowledge that they cannot easily articulate through explicit specifications. When experts modify AI-generated artifacts by correcting terminology, restructuring arguments, and adjusting emphasis, these edits reveal domain understanding that remains latent in traditional prompt-based interactions. Current systems treat such modifications as endpoint corrections rather than as implicit specifications that could reshape subsequent reasoning. We propose context-mediated domain adaptation , a paradigm where user modifications to system-generated artifacts serve as implicit domain specification that reshapes Large Language Model (LLM)-powered multi-agent reasoning behavior. Through our system Seedentia , a web-based multi-agent framework for sense-making, we demonstrate bidirectional semantic links between generated artifacts and system reasoning. Our approach enables specification bootstrapping where vague initial prompts evolve into precise domain specifications through iterative human-AI collaboration, implicit knowledge transfer through reverse-engineered user edits, and in-context learning where agent behavior adapts based on observed correction patterns. We present results from an evaluation with domain experts who generated and modified research questions from academic papers. Our system extracted 46 domain knowledge entries from user modifications, demonstrating the feasibility of capturing implicit expertise through edit patterns, though the limited sample size constrains conclusions about systematic quality improvements.

Onboarding a user to a visualization dashboard entails explaining its various components, including the chart types used, the data loaded, and the interactions available. Authoring such an onboarding experience is time-consuming and requires significant knowledge and little guidance on how best to complete this task. Depending on their levels of expertise, end users being onboarded to a new dashboard can be either confused and overwhelmed or disinterested and disengaged. We propose interactive dashboard tours (D-Tours) as semi-automated onboarding experiences that preserve the agency of users with various levels of expertise to keep them interested and engaged. Our interactive tours concept draws from open-world game design to give the user freedom in choosing their path through onboarding. We have implemented the concept in a tool called D-TOUR Prototype, which allows authors to craft custom interactive dashboard tours from scratch or using automatic templates. Automatically generated tours can still be customized to use different media (e.g., video, audio, and highlighting) or new narratives to produce an onboarding experience tailored to an individual user. We demonstrate the usefulness of interactive dashboard tours through use cases and expert interviews. Our evaluation shows that authors found the automation in the D-Tour Prototype helpful and time-saving, and users found the created tours engaging and intuitive. This paper and all supplemental materials are available at https://osf.io/6fbjp/.

Many long-established, traditional manufacturing businesses are becoming more digital and data-driven to improve their production. These companies are embracing visual analytics in these transitions through their adoption of commercial dashboarding systems. Although a number of studies have looked at the technical challenges of adopting these systems, very few have focused on the socio-technical issues that arise. In this paper, we report on the results of an interview study with 17 participants working in a range of roles at a long-established, traditional manufacturing company as they adopted Microsoft Power BI. The results highlight a number of socio-technical challenges the employees faced, including difficulties in training, using and creating dashboards, and transitioning to a modern digital company. Based on these results, we propose a number of opportunities for both companies and visualization researchers to improve these difficult transitions, as well as opportunities for rethinking how we design dashboarding systems for real-world use.

Spreadsheet-based tools provide a simple yet effective way of calculating values, which makes them the number-one choice for building and formalizing simple models for budget planning and many other applications. A cell in a spreadsheet holds one specific value and gives a discrete, overprecise view of the underlying model. Therefore, spreadsheets are of limited use when investigating the inherent uncertainties of such models and answering what-if questions. Existing extensions typically require a complex modeling process that cannot easily be embedded in a tabular layout. In Fuzzy Spreadsheet, a cell can hold and display a distribution of values. This integrated uncertainty-handling immediately conveys sensitivity and robustness information. The fuzzification of the cells enables calculations not only with precise values but also with distributions, and probabilities. We conservatively added and carefully crafted visuals to maintain the look and feel of a traditional spreadsheet while facilitating what-if analyses. Given a user-specified reference cell, Fuzzy Spreadsheet automatically extracts and visualizes contextually relevant information, such as impact, uncertainty, and degree of neighborhood, for the selected and related cells. To evaluate its usability and the perceived mental effort required, we conducted a user study. The results show that our approach outperforms traditional spreadsheets in terms of answer correctness, response time, and perceived mental effort in almost all tasks tested.

In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups are accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

Abstract Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non‐expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real‐world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems.