Asking the Curriculum: Transforming Accreditation through Course-Grounded AI

Accreditation and program review are intended to ensure educational quality, but they often rely on labor-intensive, episodic efforts to assemble evidence of student learning. Faculty frequently spend hundreds of hours collecting course materials to produce static reports that are difficult to update or act upon. We present an approach we have been exploring: treating institutional course content as a system of record for learning—a continuously updated, institution-owned source of evidence showing what students are learning, where it occurs, and how well outcomes are achieved—enabled by course-grounded artificial intelligence. Rather than relying on manually curated or sampled artifacts, this approach draws from the full body of course materials and makes evidence continuously available. Through ongoing collaborations with the Schools of Business and Dentistry at the University of Michigan, we examine how AI-powered curriculum mapping can reduce faculty effort, provide evidence-based answers to where outcomes are taught and assessed, and surface connections between outcomes, assignments, rubrics, and student performance. This enables leaders to “ask their curriculum” such questions as: Where are we assessing ethical reasoning? What evidence supports this mapping? By shifting from sampled to comprehensive evidence and from static reports to continuously available insight, this approach begins to reshape accreditation as a more continuous, evidence-driven process.