This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard's CS171 data visualization course. While previous studies have focused on GPT's ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT's abilities in various visualization tasks, such as data interpretation, visualization design, visual data exploration, and insight communication. The evaluation utilized GPT-3.5 and GPT-4 to complete assignments of CS171, and included a quantitative assessment based on the established course rubrics, a qualitative analysis informed by the feedback of three experienced graders, and an exploratory study of GPT's capabilities in completing border visualization tasks. Findings show that GPT-4 scored 80% on quizzes and homework, and TFs could distinguish between GPT- and human-generated homework with 70% accuracy. The study also demonstrates GPT's potential in completing various visualization tasks, such as data cleanup, interaction with visualizations, and insight communication. The paper concludes by discussing the strengths and limitations of GPT in data visualization, potential avenues for incorporating GPT in broader visualization tasks, and the need to redesign visualization education.

翻译：本文对生成式预训练Transformer（GPT）模型在哈佛大学CS171数据可视化课程中的表现进行了实证评价。以往研究主要关注GPT生成可视化代码的能力，而本研究则超越代码生成范畴，评估了GPT在多种可视化任务中的能力，包括数据解读、可视化设计、视觉数据探索以及洞见传达。评价采用GPT-3.5和GPT-4完成CS171课程作业，并包含基于既定课程评分标准的量化评估、基于三位经验丰富评分员反馈的定性分析，以及针对GPT完成更广泛可视化任务能力的探索性研究。结果表明，GPT-4在测验和作业中得分达80%，助教区分GPT生成作业与人类作业的准确率为70%。研究还展示了GPT在完成数据清理、可视化交互及洞见传达等各类可视化任务中的潜力。本文最后讨论了GPT在数据可视化中的优势与局限，将GPT融入更广泛可视化任务的潜在途径，以及重新设计可视化教育的必要性。