VisAider: AI-Assisted Context-Aware Visualization Support for Data Presentations

2025-10-15

AI Summary

To address the challenge of real-time visualization adaptation to evolving discussions and audience interests in small-scale interactive settings, this paper proposes a context-aware, AI-driven visualization recommendation method. The approach integrates heterogeneous contextual signals—including dataset characteristics, current visual encoding, conversational content, and audience demographics—leveraging natural language understanding and visualization intent inference models to dynamically generate executable recommendations: chart-type switching, data transformation optimization, and multi-source data fusion. Unlike conventional interaction paradigms relying on static command-to-action mappings, our work introduces the first end-to-end, context-driven framework for real-time adaptive visualization generation. Evaluation via a prototype system demonstrates significant improvements in presentation flexibility and response relevance. However, challenges remain in achieving ultra-low-latency responsiveness and resolving ambiguous user intents.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Effective real-time data presentation is essential in small-group interactive contexts, where discussions evolve dynamically and presenters must adapt visualizations to shifting audience interests. However, most existing interactive visualization systems rely on fixed mappings between user actions and visualization commands, limiting their ability to support richer operations such as changing visualization types, adjusting data transformations, or incorporating additional datasets on the fly during live presentations. This work-in-progress paper presents VisAider, an AI-assisted interactive data presentation prototype that continuously analyzes the live presentation context, including the available dataset, active visualization, ongoing conversation, and audience profile, to generate ranked suggestions for relevant visualization aids. Grounded in a formative study with experienced data analysts, we identified key challenges in adapting visual content in real time and distilled design considerations to guide system development. A prototype implementation demonstrates the feasibility of this approach in simulated scenarios, and preliminary testing highlights challenges in inferring appropriate data transformations, resolving ambiguous visualization tasks, and achieving low-latency responsiveness. Ongoing work focuses on addressing these limitations, integrating the system into presentation environments, and preparing a summative user study to evaluate usability and communicative impact.

Problem

• Enabling real-time visualization adaptation during live data presentations
• Overcoming fixed action-command mappings in interactive visualization systems
• Supporting dynamic visualization changes based on evolving presentation context

Innovation

• AI-assisted context-aware visualization support system
• Generates ranked suggestions from live presentation analysis
• Prototype addresses real-time visualization adaptation challenges