  The proliferation of smart mobile devices has spurred an explosive growth of
mobile crowd-learning services, where service providers rely on the user
community to voluntarily collect, report, and share real-time information for a
collection of scattered points of interest. A critical factor affecting the
future large-scale adoption of such mobile crowd-learning applications is the
freshness of the crowd-learned information, which can be measured by a metric
termed ``age-of-information'' (AoI). However, we show that the AoI of mobile
crowd-learning could be arbitrarily bad under selfish users' behaviors if the
system is poorly designed. This motivates us to design efficient reward
mechanisms to incentivize mobile users to report information in time, with the
goal 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 and
congestion performances in terms of price of anarchy (PoA), which characterizes
the degradation of the system efficiency due to selfish behavior of users.
Remarkably, we show that the proposed mechanism achieves the optimal AoI
performance asymptotically in a deterministic scenario. Further, we prove that
the proposed mechanism achieves a bounded PoA in general stochastic cases, and
the bound only depends on system parameters. Particularly, when the service
rates of PoIs are symmetric in stochastic cases, the achieved PoA is
upper-bounded by $1/2$ asymptotically. Collectively, this work advances our
understanding of information freshness in mobile crowd-learning systems.
