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The proliferation of smart mobile devices has spurred an explosive growth ofmobile crowd-learning services, where service providers rely on the usercommunity to voluntarily collect, report, and share real-time information for acollection of scattered points of interest. A critical factor affecting thefuture large-scale adoption of such mobile crowd-learning applications is thefreshness of the crowd-learned information, which can be measured by a metrictermed ``age-of-information'' (AoI). However, we show that the AoI of mobilecrowd-learning could be arbitrarily bad under selfish users' behaviors if thesystem is poorly designed. This motivates us to design efficient rewardmechanisms to incentivize mobile users to report information in time, with thegoal of keeping the AoI and congestion level of each PoI low. Toward this end,we consider a simple linear AoI-based reward mechanism and analyze its AoI andcongestion performances in terms of price of anarchy (PoA), which characterizesthe degradation of the system efficiency due to selfish behavior of users.Remarkably, we show that the proposed mechanism achieves the optimal AoIperformance asymptotically in a deterministic scenario. Further, we prove thatthe proposed mechanism achieves a bounded PoA in general stochastic cases, andthe bound only depends on system parameters. Particularly, when the servicerates of PoIs are symmetric in stochastic cases, the achieved PoA isupper-bounded by $1/2$ asymptotically. Collectively, this work advances ourunderstanding of information freshness in mobile crowd-learning systems.

Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?