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Bridging Theory and Practice: A Multiphase Study of GenAI-Assisted Visualization Learning.
IEEE Computer Graphics and Applications
|September 29, 2025
Summary
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.
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.
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