AI for Professors: Course Design

Course design begins with learning goals: what should students understand or be able to do by the end of a module? AI can help brainstorm an outline, suggest sequencing, draft examples, generate practice questions, or adapt explanations for different prior knowledge. These outputs are drafts, not evidence that students will learn more or that the material is accurate. A useful workflow starts with a syllabus, course outcomes, constraints, and authoritative source materials. Ask the tool for a proposed sequence or set of activities, then map each item back to an outcome. Verify citations, facts, and reading availability. Models may invent references, summarize a source incorrectly, or omit necessary prerequisites. For technical or specialized topics, check primary literature, textbooks, and current documentation. Assessment design requires particular care. AI can draft question variants or rubric language, but faculty should confirm that each item measures the intended skill, has a defensible answer, and does not introduce bias or ambiguous wording. Avoid uploading confidential exams or student work to unapproved services. Use sample or de-identified material for tool evaluation. Accessibility and inclusion should be part of review. Check reading level, screen-reader structure, examples, language, and assumptions about devices or prior experience. An AI-generated “simplification” can remove important nuance, while an example may stereotype students or cultures. Ask students and colleagues for feedback where appropriate. Course policies should state whether and how students may use AI and how assignments will be evaluated. Faculty should also tell students when AI contributed to course materials if institutional norms require it. Keep version history for major materials and update them when readings, tools, or course requirements change. AI can reduce drafting time, but academic judgment remains with instructors.