Faculty across Maryland higher education are actively exploring how generative AI may change teaching, learning, assessment, academic integrity, course design, and student engagement. As these practices emerge, faculty need more than tool demonstrations, prompt examples, or broad speculation. They need tested, course-based examples that show what instructors are trying, what appears to support student learning, what challenges arise, and how those practices might be adapted in other teaching contexts. 

The University System of Maryland Kirwan Center for Academic Innovation is seeking contributions to Generative AI Pedagogy in Practice: Evidence-Informed Teaching Cases from Maryland Higher Education, an openly licensed edited collection of at least 20 generative AI pedagogy interventions from Maryland higher education. The collection will feature short, reflective, evidence-informed essays from faculty and instructional educators who have implemented or piloted AI-related teaching practices in authentic course or instructional contexts. 

• Review the full call for abstracts (due November 6, 2026).
• Register for the info session on October 13, 2026, at 1 PM. 

The final collection will be published as an open educational resource in July 2027. Editorial framing will introduce the collection, synthesize patterns across essays, and make a broader argument for evidence-informed innovation in AI pedagogy.