Developing Students’ Statistical Expertise Through Writing in the Age of AI
As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-07-01
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| Series: | Journal of Statistics and Data Science Education |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26939169.2025.2497547 |
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| author | Laura S. DeLuca Alex Reinhart Gordon Weinberg Michael Laudenbach Sydney Miller David West Brown |
| author_facet | Laura S. DeLuca Alex Reinhart Gordon Weinberg Michael Laudenbach Sydney Miller David West Brown |
| author_sort | Laura S. DeLuca |
| collection | DOAJ |
| description | As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In our study, we engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Our results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist? Supplementary materials for this article are available online. |
| format | Article |
| id | doaj-art-d9a984c28b2b4d0d853124d2712ba535 |
| institution | Kabale University |
| issn | 2693-9169 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Statistics and Data Science Education |
| spelling | doaj-art-d9a984c28b2b4d0d853124d2712ba5352025-08-20T03:28:00ZengTaylor & Francis GroupJournal of Statistics and Data Science Education2693-91692025-07-0133326627810.1080/26939169.2025.2497547Developing Students’ Statistical Expertise Through Writing in the Age of AILaura S. DeLuca0Alex Reinhart1Gordon Weinberg2Michael Laudenbach3Sydney Miller4David West Brown5Department of English, Carnegie Mellon University, Pittsburgh, PADepartment of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PADepartment of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PADepartment of Humanities and Social Sciences, New Jersey Institute of Technology, Newark, NJDepartment of English, Carnegie Mellon University, Pittsburgh, PADepartment of English, Carnegie Mellon University, Pittsburgh, PAAs large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In our study, we engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Our results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist? Supplementary materials for this article are available online.https://www.tandfonline.com/doi/10.1080/26939169.2025.2497547Artificial intelligenceChatGPTData science educationGenerative AIStatistics educationWriting to learn |
| spellingShingle | Laura S. DeLuca Alex Reinhart Gordon Weinberg Michael Laudenbach Sydney Miller David West Brown Developing Students’ Statistical Expertise Through Writing in the Age of AI Journal of Statistics and Data Science Education Artificial intelligence ChatGPT Data science education Generative AI Statistics education Writing to learn |
| title | Developing Students’ Statistical Expertise Through Writing in the Age of AI |
| title_full | Developing Students’ Statistical Expertise Through Writing in the Age of AI |
| title_fullStr | Developing Students’ Statistical Expertise Through Writing in the Age of AI |
| title_full_unstemmed | Developing Students’ Statistical Expertise Through Writing in the Age of AI |
| title_short | Developing Students’ Statistical Expertise Through Writing in the Age of AI |
| title_sort | developing students statistical expertise through writing in the age of ai |
| topic | Artificial intelligence ChatGPT Data science education Generative AI Statistics education Writing to learn |
| url | https://www.tandfonline.com/doi/10.1080/26939169.2025.2497547 |
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