Artificial intelligence is reshaping higher education by enabling personalized support for students and efficiency gains for faculty. Yet, meaningful adoption requires more than technology: it demands alignment with pedagogy, careful governance of data and sources, and demonstrable educational value. This thesis investigates how AI can support university-level teaching and learning along two axes: student support and faculty support; then translates the resulting design principles into a working, course-specific AI assistant for the Analysis and Management of Production Systems (AMPS) course. A structured research methodology (Chapter 3) was applied to build the theoretical foundation (Chapter 2) and the cross-case synthesis of applications (Chapter 4), drawing primarily on Scopus-indexed literature with explicit inclusion/exclusion criteria and coding.