Ivan Bar
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 48
Abstract
The rise of large language models (LLMs) has prompted instructors in higher education to reconsider their approaches to course design, assessment strategies, and in-class technology policies. Building on work that positions syllabi as students’ first impression of an instructor’s pedagogical approach and a key site of socialization into expected AI use (Tong et al. 2025), this project investigates how university syllabi have evolved in response to the advent of AI and evaluates how effective automated approaches are for studying these changes at scale. Through the analysis of university syllabi, RAs coded AI policies, grade-weighting, in-class technology restrictions, course objectives, and assignment types. Their annotations serve both as ground-truth labels and as training data as we evaluate automated approaches. We measure how the content of these policies has shifted over time and use that information to link it to instructors’ attitudes in regard to syllabi policies. In particular, we examine whether instructors have chosen to shift toward assignment types that are less prone to AI-assisted work. We also analyze if explicit AI policies correlate with other syllabi policies, such as stricter in-class technology restrictions or greater structural redesign.
