This paper provides an overview of the development of the Global Assimilative Ionospheric Model (GAIM) by a team of investigators from the University of Southern California (USC) and the Jet Propulsion Laboratory (JPL). The USC/JPL GAIM utilizes data assimilation techniques, which are widely used in meteorological applications, for the purpose of monitoring and forecasting Earth's ionosphere. We discuss the general structure of GAIM, which includes a first-principles model of the ionosphere, a series of auxiliary models for the driving forces, a data processing subsystem, and an optimization subsystem. Two techniques for the estimation of electron density and driving forces in the ionosphere are presented: The four-dimensional variational method and the Kalman filter. Some validation methods and results are also presented. These results demonstrate the potential of GAIM in providing accurate specification of the ionosphere.