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ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent.

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    This summary is machine-generated.

    This study introduces ProactiveVA, an intelligent framework that uses a Large Language Model (LLM)-powered agent to proactively assist users with visual analytics (VA) tools. It offers context-aware help, improving the user experience in complex data analysis.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Science

    Background:

    • Visual analytics (VA) tools are essential for complex data analysis but can be overwhelming for users.
    • Current intelligent assistance in VA is reactive, only offering help when explicitly requested.
    • There is a need for more proactive and context-aware intelligent assistance in VA systems.

    Purpose of the Study:

    • To propose and evaluate ProactiveVA, a novel framework for proactive intelligent assistance in visual analytics.
    • To design an LLM-powered UI agent that monitors user interactions and delivers context-aware help.
    • To address the limitations of existing VA assistance by offering support when users need it most.

    Main Methods:

    • Conducted a formative study analyzing user interaction logs to understand help-seeking behaviors.
    • Distilled key design requirements: intent recognition, solution generation, interpretability, and controllability.
    • Developed a three-stage UI agent pipeline (perception, reasoning, acting) for autonomous user need assessment and assistance delivery.

    Main Results:

    • Implemented and demonstrated the generalizability of the ProactiveVA framework in two representative VA systems.
    • Evaluated effectiveness through algorithm evaluation, case/expert studies, and a user study.
    • Identified key design trade-offs and areas for future research in proactive VA.

    Conclusions:

    • ProactiveVA effectively provides context-aware assistance, enhancing the visual analytics user experience.
    • The proposed LLM-powered UI agent can autonomously perceive user needs and offer tailored guidance.
    • Further research is needed to explore design trade-offs and advance proactive assistance in VA.