2025/12/29 by Richard A. Levine · 1 citation
Computer Science · Mathematics · #Bayesian statistics #Data Analysis with R #Generative grammar #Generative model #Intelligent Tutoring Systems and Adaptive Learning #Statistical analysis #Statistics Education and Methodologies #Statistics education
paper · doi:10.1080/00031305.2025.2608724
published in The American Statistician 80(3), 360-371 (Taylor & Francis)
openalex created_date 2025/12/29 · openalex publication_date 2025/12/29 · openalex updated_date 2026/08/04
This article examines how students can engage with generative artificial intelligence (genAI) as collaborators in the statistics learning process. Prompt engineering is positioned as a transferable, tool-agnostic competency that reinforces core elements of statistical thinking, including clarity, iteration, and purposeful inquiry. Through illustrative collaborations, we explore applications such as automating and optimizing code, acquiring programming syntax, and designing simulation studies. While these tasks are drawn from upper-level undergraduate and graduate coursework, the running example—a Chi-squared test of association—is intended to spur ideas for incorporating genAI into the introductory statistics classroom. Supplementary materials include (a) an outline of a learning management module and structure of the discussion and activities during my class periods covering this module on responsible use of generative AI; (b) R Markdown files and compiled pdf documents intended to support classroom integration; (c) illustrative comparisons across three widely used platforms—ChatGPT, Copilot, and Gemini—to highlight how differences in output style and reasoning can inform instructional design, rather than to rank or evaluate tools technically. The article concludes with a discussion of strategies for promoting ethical, transparent, and inclusive uses of genAI in statistics education.