With item response data, systematic variation within nested groups of generated items may negatively impact the estimation of item and person parameters. This paper studies a model that can capture the multilevel structure of the data and explain within-template systematic variability. The goal of this model is twofold. First, explaining and removing non-random error may improve ability and item parameter estimates. And second, finding systematic variation can bring insights into the educational process. Simulation results are discussed at length.