George Chaney III
Research Mentor: James Dumlao
Mentor Department: Not Available, Information
Author(s): Not Available
Session: Session 1 (9:00 AM – 9:50 AM)
Presentation Type: Poster 96
Abstract
Generative artificial intelligence (GenAI) has rapidly become a major presence in higher education, raising new questions about academic integrity, critical thinking, skill development, and assessment design. Although these concerns are widely discussed, there is limited systematic evidence on how instructors articulate AI use within course syllabi and how those policies relate to course design. This project examines variation in AI-related syllabus policies at the University of Michigan and how these differences connect to broader instructional choices. Using a dataset of annotated syllabi, the study codes for features such as the presence and type of AI policies (explicit vs. implicit), the specific wording of AI-related text, and the location of these policies within syllabi, as well as course characteristics including assignment types, grading weight distributions, office hour availability, and overall course expectations. Preliminary observations suggest that stricter AI restrictions are more common in writing-intensive or discussion-based courses, while STEM and project-based courses tend to allow greater flexibility. As analysis continues, this research aims to better understand how AI policy design aligns with course structure and how these differences may influence student learning, workload, and accessibility. Ultimately, the findings will contribute to more effective and evidence-based approaches to integrating AI in higher education.
