Can Students Tell, and Does It Matter? Detecting, Evaluating, and Calibrating Instructor AI Use in the Marketing Classroom

Marketing classrooms model the professional use of generative artificial intelligence (AI), but instructor AI use also creates an educational service encounter in which students judge trust, effort, and the performance of the instructor role. Using parallel surveys from 340 students and seven instructors across 10 sections of Principles of Marketing, this study examines whether students detect instructor AI use, how they evaluate it by task and agent, and whether instructors anticipate those evaluations. Students were weak detectors: estimates of AI-generated or AI-assisted course content corresponded only modestly with instructor benchmarks and varied more within sections than between them. Detection confidence was unrelated to accuracy. At the same time, students drew meaningful evaluative boundaries. They rated AI use as more appropriate for creating materials than for grading or feedback, and rated AI below human and publisher alternatives in every task. Perceived disclosure was unrelated to trust, but perceived effort was closely associated with trust, instructor ratings, and perceived learning. Instructors viewed instructor AI use more favorably than students did and valued disclosure less. The results suggest that AI governance in marketing education should be task-specific, relationally aware, and informed by student data rather than by assumptions of student detection or instructor calibration.