Over the course of four decades, model predictive control (MPC) has become one of the great success stories in systems and control. It has grown from its native habitat (chemical process control) into all domains of control applications—power and energy systems, mechatronics and robotics, as well as aerospace and aeronautics. Hence, in a modern systems and control curriculum, MPC triggers not so much the question of if it should be taught. In fact, industrial demand for and the continued research potential of MPC suggest that one should rather ask the Aristotelian 5W1H (what, when, where, why, who, and how?) about teaching MPC. This article presents insights into the 5Ws distilled from the results of a survey on teaching MPC conducted in the systems and control community. Moreover, the how is approached through blueprint suggestions for curricula for an undergraduate discrete-time linear-quadratic MPC course and for graduate courses covering the continuous-time nonlinear avenue and the learning-based route.