This lecture will propose taking a mechanism design approach to the classroom. The classroom is appealing as a domain for mechanism design as many researchers are also instructors in the classroom. A central concept in mechanism design for the classroom is scoring rules. We will review scoring rules as mechanisms for eliciting beliefs, and propose an optimization framework for identifying good scoring rules. This optimization framework admits many the research directions that have been studied for traditional mechanism design, such as simple versus optimal, robustness to prior beliefs, and sample complexity. Some results will be taken from joint work with Yingkai Li, Liren Shan, and Yifan Wu.