"Code Is Cheap. Show Me the Talk.": Lessons from Teaching and Managing AI Coding Tool Usage in a Visualization Course

2026-07-10

AI Summary

This study addresses the challenge of guiding advanced students in visualization courses to use generative AI programming tools responsibly, ensuring they do not bypass core learning objectives or produce homogenized work. Through carefully designed prompt injections, oral checkpoint questions, and two structured teaching interventions—complemented by prompt log analysis and verbal assessments—the research offers the first systematic reflection on the real-world impact of AI tools in visualization education. Findings reveal that while student projects became more polished, their visual styles converged significantly; over half relied heavily on scaffolded prompts lacking explanatory content. In response, the study proposes three pedagogical strategies: clearly defining acceptable boundaries for AI use, strengthening prompt engineering instruction, and cultivating students’ critical capacity to adapt and evaluate AI-generated design outputs.

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.

Problem

• Generative AI
• visualization education
• AI coding tools
• learning trajectories
• prompting

Innovation

• Generative AI
• prompt engineering
• visualization education
• AI coding tools
• oral checkout