发表机构
University of Maryland College Park（马里兰大学帕克分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
在可视化课程中分享管理和教授人工智能使用的经验，实施提示注入等，发现学生使用情况及问题，指出需明确人工智能使用界限、提示指导，教学生质疑并适应通用设计。
AI 中文摘要
生成式人工智能（GenAI）编码工具正在改变可视化教育。它们既能协助实现和设计，也可能让学生绕过预期的学习轨迹。本文分享了我们在一门高级可视化课程中管理和教授人工智能使用的回顾性经验。我们实施了提示注入，提出口头检查问题，并教授了两个人工智能编码实验室。在我们的编码实验室之前，至少一半的学生已经在作业中使用了人工智能工具。在两个人工智能编码实验室中，细化约占学生提示日志的一半，而解释几乎没有。在人工智能编码为可选的实验室中，78份提交中有44份（56.4%）的作品更喜欢脚手架式指令而不是设计自己的提示。学生的最终项目比上一次更精致，但视觉上也更同质化。我们的反思表明需要更明确的人工智能使用界限和提示指导，以及教导学生质疑通用人工智能设计并使其适应自己的数据和故事。
英文摘要
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.
Comments6 Figures, 5 Pages
Journal refIEEE VIS 2026 Educator Report
