By harnessing the capabilities of unmanned aerial and ground vehicles (UAVs and UGVs), equipped with high-precision sensors, air-ground mobile crowdsensing (AG-MCS) has proven to be effective for data collection in urban environments. In this paper, by optimizing the metric of age-of-information (AoI) that measures the freshness of collected data, we consider the problem of AoI-Aware AG-MCS (A3G-MCS), where UGVs dispatch UAVs from multiple UGV stops to collect data from point-of-interests (PoIs). We propose a novel multi-agent curriculum learning framework called “MACL(MCS)”, that explicitly balances the individual and team goals of both UAV/UGV controllers to facilitate the exploration of policy towards globally-optimal performance. It is further enhanced by a UAV/UGV collaborative observation augmentation (COA) module for improved inter-controller communication. Extensive results reveal that MACL(MCS) consistently outperforms five baselines, and achieves comparable performance to exact method with better scalability and efficiency. It also showcases strong generalization capability towards real-world scenarios on both TSPLIB and Purdue, KAIST and NCSU datasets.