College/Department: Department of Biostatistics
Course: Biostatistics
Student Enrollment: 100
Proposal: My course is a required statistics course for most Biology majors, most of whom are pre-health. The main learning goals are for students to be able to describe, analyze, and visualize different types of biological data using appropriate statistical techniques and the statistical programming language R. The textbook I use has a series of R "labs" that students work through in recitation sections with TAs, and I also discuss relevant R functions in class and my pre-class videos as time allows. The students use R for their final team projects, which involve developing short reports and presentations on the results of statistical tests they design based on provided real-world biological datasets. In the past I have worked with LTS to provide students with uniform R installation via Kubernetes on the university HPC. Since I last taught the class in spring 2023 generative AI has exploded, and there are now numerous ways for students to use generative AI tools in writing code, as well as (to a somewhat lesser extent) in analyzing data using statistics.
I am hoping to integrate generative AI into teaching and learning in this class, and to provide guidance to students on the benefits and limitations of these tools, while also keeping in mind considerations of student privacy and environmental costs. Students will have access to some version of these tools throughout their lives, and I would like them to be able to use the tools effectively and ethically, while at the same time not losing the opportunity to develop the kind of "programming intuition" that will help them assess when and how the tools might lead them astray. Leaning into teaching about generative AI is thus critical.
CITL/I&O Collaborators: Justin Greenlee, Jeremy Mack, Rob Weidman & Tricia Martone