"Code Is Cheap. Show Me the Talk.": Lessons from Teaching and Managing AI Coding Tool Usage in a Visualization Course
2026-07-10 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors looked at how using AI coding tools affected a college visualization class. They found that many students used AI early on, especially for refining code, but rarely asked it to explain things. When given a choice, most students used guided instructions instead of creating their own AI prompts. While projects looked nicer than before, they also tended to look more similar to each other. The authors suggest teaching clearer AI usage rules and helping students learn to customize AI outputs for their specific data and stories.
Generative Artificial Intelligencevisualization educationprompt engineeringAI coding labsstudent learning trajectoriescode refinementscaffolded instructionsAI use boundariesproject designdata storytelling
Authors
Zhongzheng Xu, Taehyun Yang, Fumeng Yang
Abstract
Generative Artificial Intelligence (GenAI) coding tools are transforming visualization education. They can assist with implementation and design, but they can also let students bypass intended learning trajectories. In this paper, we share our retrospective experience managing and teaching AI use in an upper-level visualization course. We implemented prompt injections, asked oral checkout questions, and taught two AI coding labs. Prior to our coding labs, at least half of the students had already used AI tools in their assignments. In both AI coding labs, refinement accounted for about half of students' prompting logs, and explanation was almost absent. In the lab where AI coding was optional, 44 of 78 (56.4%) submissions preferred the scaffolded instructions over designing their own prompts. Students' final projects were more polished than in our previous offering, but also more visually homogeneous. Our reflections point to the need for clearer AI use boundaries and instruction on prompting, and for teaching students to question generic AI designs and adapt them to their data and story.