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Promises and Pitfalls: Using Large Language Models to Generate Visualization Items.

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    This study introduces the VILA pipeline, using large language models (LLMs) to automatically generate diverse visualization items for assessing visualization literacy. The VILA bank offers over 1,000 items, demonstrating potential for efficient educational material creation.

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    Area of Science:

    • Information Visualization
    • Educational Technology
    • Artificial Intelligence in Education

    Background:

    • Visualization items are crucial for assessing visualization literacy but are time-consuming to create.
    • Existing methods for generating visualization items lack scalability and diversity.
    • Researchers need large, high-quality item banks for robust educational and evaluative studies.

    Purpose of the Study:

    • To investigate the potential of large language models (LLMs) for automating the generation of multiple-choice visualization items.
    • To develop and evaluate a pipeline (VILA) for creating diverse visualization items.
    • To establish a reliable bank of visualization items for research and education.

    Main Methods:

    • Developed the VILA (Visualization Items Generated by Large LAnguage Models) pipeline through an iterative design process.
    • Generated 1,404 candidate items across 12 chart types and 13 visualization tasks.
    • Collaborated with 11 visualization experts to create an evaluation rulebook and rate item quality.

    Main Results:

    • Created the VILA bank containing approximately 1,100 high-quality visualization items.
    • Identified and classified limitations of the VILA pipeline and the importance of human oversight.
    • Developed VILA-VLAT, a visualization literacy test with moderate to high convergent validity (R = 0.70) compared to existing tests.

    Conclusions:

    • The VILA pipeline offers an efficient method for generating diverse visualization items, addressing a critical need in visualization education and research.
    • The VILA bank provides a valuable resource for assessing visualization literacy.
    • Future work should focus on refining LLM-based item generation and exploring broader applications.