Abstract

Researchers have studied statistical anxiety in education for decades, but Large Language Models are relatively new. LLMs can help students to understand terms and processes, but they can also hinder them by providing an easy way to get to the answer without learning how to do it themselves. While higher education instructors maybe apprehensive about students’ use of AI and LLMs, education professionals have little understanding of how students use AI and how it relates to important constructs such as motivation and anxiety. This paper focuses on three research questions related to self-regulated learning theory. How is AI use related to statistical anxiety? How is AI use related to motivation? How do students' prompts differ based on types of motivation and statistical anxiety? Survey responses from the Motivated Strategies for Learning Questionnaire and the Statistical Anxiety Survey – Revised were gathered from two large enrollment online introductory courses: Statistics 1 and Statistics 2. The mixed-methods analysis will include quantitative analyses of relationships among AI use, motivation, and statistical anxiety, as well as a thematic analysis of prompt types. Limitations, takeaways for instructors and ideas for future research will be given.