2026-10-12T13:05:11Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33080872 2026-09-28T18:41:49Z category_7 category_21 category_45 category_64 category_106 portal_87 item_type_6 month_year_09_2026

Self-Reported LLM Usage and Outcomes on a Data Science Project: Evidence from Two Undergraduate Courses Rohan Alexander (5876153) Luca Carnegie (24452839) Nathalie Moon (6249263) Medicine Biotechnology Sociology Cancer Science Policy Academic performance Data science education Large language models Self-reported tool usage <p>To help understand the effect of Large Language Models (LLMs) on data science practice we examine the extent to which self-reported LLM usage is correlated with the mark that a student received on a final paper in a classroom data science setting. We find some mild evidence from this observational study that LLM usage may be associated with better scores, especially for students who do not natively speak English. Additionally, comparing self-reported usage, there was a considerable increase between the class that occurred in January-April 2024 where 41% of students self-reported extensive LLM usage and the class that occurred in September-December 2024 where 69% reported extensive LLM usage. Despite the classroom setting used for evaluation, the task of interest is similar to the work done by professional data scientists. Our finding suggests the need for more extensive work evaluating how LLMs can be integrated into the data science workflow in a way that provides value in both the classroom and the workplace.</p> 2026-09-28T18:41:49Z Text Journal contribution 10.6084/m9.figshare.33080872.v2 https://figshare.com/articles/journal_contribution/Self-Reported_LLM_Usage_And_Outcomes_On_A_Data_Science_Project_Evidence_From_Two_Undergraduate_Courses/33080872 CC BY 4.0