Artificial intelligence enhanced instruction for mastering statistical uncertainty

The National Science Foundation Division of Undergraduate Education awarded CSU Fullerton Auxiliary Services Corporation $395,509 on October 1, 2026, to conduct a Level 1 IUSE ESL project exploring how artificial intelligence tools support student reasoning in undergraduate statistics courses. The project, running through September 30, 2029, will implement two matched instructional modules in high-enrollment general education statistics courses at California State University, Fullerton, comparing AI-supported learning against non-AI scaffolds. Students in nearly identical course assignments will receive either guided, non-answer-giving generative AI prompts or traditional non-AI scaffolds embedded in course materials. The project will isolate the effect of AI support by measuring student approaches and outcomes through surveys, pre- and post-course assessments, in-class quizzes, and end-activity reflections. An experienced project evaluator will provide formative and summative feedback throughout implementation. The award funds work performed in Fullerton, California, and operates under the NSF STEM Education program (CFDA 47.076), which supports research and development to improve STEM education effectiveness for all students through the Engaged Student Learning Track.

Abstract

This level 1 iuse ESL project from california state university, fullerton aims to serve the national interest by exploring how artificial intelligence (AI) tools can be used to support student reasoning in undergraduate statistics courses. There is significant research about how interactive classroom technologies can support students? Conceptual understanding. As AI usage becomes more widespread, however, IT is important to distinguish how AI-based supports perform relative to other established, non-AI tools. This project will explore this important question by implementing two matched instructional modules in high-enrollment general education statistics courses. Current research highlights the potential risks and rewards of AI use in undergraduate stem courses. Using AI tools to support and guide, instead of replace, student thinking can be a powerful approach to strengthen student learning and understanding. This project will isolate the effect of AI-supported learning opportunities by comparing student approaches and outcomes on two nearly identical course assignments. The versions will differ only in the form of support provided, either guided, non-answer-giving generative AI prompts or non-AI scaffolds embedded in course materials. Data streams will include surveys, pre/post course, assessments, in-class quizzes, and end-activity reflections. An experienced project evaluator will provide formative and summative feedback over the life of the project to strengthen implementation and progress towards project goals. The NSF iuse: EDU program supports research and development projects to improve the effectiveness of stem education for all students. Through the engaged student learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are planned for this award.