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  3. Bridging Theory And Practice: A Multiphase Study Of Genai-assisted Visualization Learning.
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  3. Bridging Theory And Practice: A Multiphase Study Of Genai-assisted Visualization Learning.

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Bridging Theory and Practice: A Multiphase Study of GenAI-Assisted Visualization Learning.

Mak Ahmad, Kwan-Liu Ma, Beatriz Sousa Santos

    IEEE Computer Graphics and Applications
    |September 29, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Structured generative AI (Artificial Intelligence) exposure enhances student engagement and tool adoption in data visualization education. This approach, grounded in constructivist learning, maintains rigor and offers practical frameworks for AI-augmented curricula.

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    Area of Science:

    • Educational Technology
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Generative AI is transforming technical education, necessitating new approaches to teaching visualization skills.
    • Understanding student learning in AI-augmented environments is crucial for curriculum development.

    Purpose of the Study:

    • To examine the impact of structured large language model (LLM) exposure on data visualization education.
    • To identify how students integrate AI tools into their visualization workflows.

    Main Methods:

    • A mixed-methods study involving 65 graduate students (data science and computer science) across two universities.
    • A multiphase investigation including preassessments, intervention observations, assignment reflections, and postintervention evaluations.
  • Utilized Observable's AI Assist platform for structured generative AI exposure.

    • Main Results:

    • Structured generative AI exposure, aligned with constructivist principles, fostered sustained student engagement and tool adoption.
    • Identified specific patterns in student integration of AI into visualization workflows.
    • Maintained pedagogical rigor throughout the intervention.

    Conclusions:

    • Generative AI can be effectively integrated into visualization curricula to support student learning.
    • Provides practical frameworks and evidence-based insights for scaffolding AI-assisted learning in visualization.
    • Demonstrates initial evidence of sustained impact of AI assistance on learning outcomes over three weeks.