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

Understanding how students learn visualization skills is becoming increasingly crucial as generative AI transforms technical education. We present a systematic study examining how structured exposure to large language models via Observable’s AI Assist platform impacts data visualization education through a multiphase investigation across two universities. Our mixed-methods approach with 65 graduate students (32 data science and 33 computer science) revealed that structured generative AI exposure following constructivist learning principles enabled sustained engagement and tool adoption while maintaining pedagogical rigor. Through a structured multiphase study incorporating preassessments, intervention observations, detailed assignment reflections, and postintervention evaluation within the academic term constraints, we identified specific patterns in how students integrate generative AI into their visualization workflows. The results from our mixed-methods analysis suggest potential strategies for adapting visualization education to an AI-augmented future while preserving essential learning outcomes. We contribute practical frameworks for integrating generative AI tools into visualization curricula and evidence-based insights on scaffolding student learning with AI assistance, with initial evidence of sustained impact over a three-week period following instruction.