Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities
🤖 AI Summary
This study addresses core challenges in human–data interaction in the AI era, including perceptual latency, limited scalability, outdated interaction paradigms, and insufficient reliability and interpretability of generative outputs. It systematically examines the impact of AI technologies on human–data interaction, exploration, and visualization, redefining the role of humans in intelligent analysis. Integrating insights from cognitive science, perceptual theory, and interaction design principles, the work proposes a human-centered interaction framework that synergizes large language models (LLMs), vision-language models (VLMs), and multimodal visualization techniques. Moving beyond traditional evaluation metrics focused primarily on efficiency and scalability, this research advances a cognition-driven analytical paradigm for human–AI collaboration and articulates design principles and future research directions for human-centric intelligent analytics systems in the AI era.
Application Category
📝 Abstract
The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is predominantly unstructured, as well as foundation models such as LLMs and VLMs, which introduce additional uncertainty into analytical processes. These shifts expose persistent challenges for human-data interactive systems, including perceptually misaligned latency, scalability constraints, limitations of existing interaction and exploration paradigms, and growing uncertainty regarding the reliability and interpretability of AI-generated insights. Responding to these challenges requires moving beyond conventional efficiency and scalability metrics, redefining the roles of humans and machines in analytical workflows, and incorporating cognitive, perceptual, and design principles into every level of the human-data interaction stack. This paper investigates the challenges introduced by recent advances in AI and examines how these developments are reshaping the ways users engage with data, while outlining limitations and open research directions for building human-centered AI systems for interactive data analysis in the AI era.
Problem
Research questions and friction points this paper is trying to address.
Human-Data Interaction
AI-generated insights
Uncertainty
Interactive data analysis
Foundation models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Human-Data Interaction
Foundation Models
Visual Analytics
Uncertainty in AI
Human-Centered AI
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