A study was conducted to investigate whether augmenting the calibration of items using computerized adaptive test (CAT) data matrices produced estimates that were unbiased and improved the stability of existing item parameter estimates. Item parameter estimates from four pools of items constructed for operational use were used in the study to arrive at a final number of 1,392 unique items. Fifty sets of true parameter estimates were generated from the base item prior information, and each true set served as the parameter estimated for a CAT simulation that incorporated content constraints and exposure control. One thousand test takers were simulated at each of 41 points on the ability scale. The examination of the influence of CAT data on item response theory three-parameter logistic model (3PL) item characteristic curves and on item parameter estimates for the 3PL model in a Bayesian context showed that the error in the item characteristic curve as a whole and in the estimates of item difficulty appeared to be reduced when CAT data information was incorporated into the estimation of the parameter. Results suggest that error in estimating item characteristic curves may be reduced by incorporating CAT information into the calibration process. (Contains 4 tables, 27 figures, and 4 references.) (SLD)