<p>In the near future, transportation systems will include both autonomous vehicles and human-operated vehicles sharing the same traffic conditions. Human drivers will have difficulty predicting the actions of autonomous vehicles and the latter will face challenges due to complex decision-making algorithms and dynamic environments. The lack of standardized interaction protocols between autonomous vehicles and human drivers further complicates safe decision-making. This paper proposes an AI-based advisory framework to enhance human driving skills in mixed autonomy traffic and improve autonomous vehicles in a Human–AI teaming fashion. Our framework is composed of both a centralized component and a decentralized component. The centralized component primarily identifies driving style and trajectory parameters that impact traffic efficiency across large-scale traffic networks shared by human drivers and autonomous vehicles. At the local level, however, our proposed framework features a decentralized, agent-based strategy to enable effective coordination between human and autonomous vehicles—especially at complex intersections. An initial prototype is modeled and implemented in a desktop virtual reality environment for testing and training. </p>