It can be labor-intensive for faculty to organize business community stakeholders to volunteer for student interviews as an overall effort to improve learning authenticity and workplace relevancy for experiential activities within supply chain courses. Higher education administrators and scholars have been encourages to explore leveraging artificial intelligence (AI) to improve curriculum design as well as increase (or maintain) student learning effectiveness and satisfaction. The authors examined satisfaction and learning effectiveness in a logistics course where students were given the choice of finding and interviewing a business community stakeholder versus the faculty-developed AI trained model disguised as a confidential informant of a local business. Grade and student satisfaction were compared across the two experiential learning activity. Since the graduate student cohort were relatively young, the authors expected there to be no difference between learning effectiveness (since the assignment was identical - only the data collection differed), which was the finding. The authors anticipated the students would prefer interviewing a real local business stakeholder more than the AI model, but surprisingly, students preferred the latter. The authors examined formative and summative data to quantitatively compared student learning effectiveness and satisfaction between the two experiential learning activities. The authors then interpreted the results, rationalized the differences, and proposed implications for consideration.

Keywords: AI in education, experiential learning, student satisfaction, learning effectiveness, supply chain education, logistics course, curriculum design, artificial intelligence, business stakeholder interviews, higher education innovation.