This presentation considers a design-centered process for enhancing OER discovery that positions a course syllabus as the primary artifact for leveraging generative AI to identify open materials aligned with course goals. Beyond the typical ways to discover OER (e.g., searching repositories, listservs, guides), the approach combines two complementary strategies. First, generative AI analyzes an instructor’s syllabus by examining learning outcomes, course topics, assignment structures, and described competencies to recommend OER aligned with instructional design decisions. This syllabus-based analysis reduces the time required to locate, review, and evaluate open materials while supporting a more consistent discovery workflow for instructors, librarians, and instructional designers. Second, generative AI is used to locate syllabi from peer institutions by scanning departmental websites, institutional repositories, and other public sources. These syllabi often reference openly licensed textbooks, media, or course resources that may not be explicitly labeled as OER. As a result, they become valuable entry points for expanded discovery and help instructors situate their courses within broader disciplinary practices. Where appropriate, the process also surfaces opportunities for open pedagogy, including student co-creation of knowledge, adaptation or revision of OER, and authentic assignments with audiences beyond the course.
