Trustworthy AI involves multi-agent interactions where each agent wants to maximize their individual gains. To understand the collective objectives that are achievable and are mutually ‘best’ for all agents, we need to train our workforce with topics in strategic multi-agent systems that delve into the topics at the interface of economics and computer science. This proposal aims to develop an impactful yet easy-to-follow set of video-learning materials and an associated grading platform to help students and practitioners to self-train on the topics of algorithmic game theory and mechanism design.