A simulation study was conducted to investigate the application of expected a posteriori (EAP) trait estimation in computerized adaptive tests (CAT) based on the generalized partial credit model (Muraki, 1992), and to compare the performance of EAP with maximum likelihood trait estimation (MLE). The performance of EAP was evaluated under different conditions: the number of quadrature points (10, 20, and 30), and the type of prior distribution (normal, uniform, negatively skewed, and positively skewed). The relative performance of the MLE and EAP estimation methods were assessed under two distributional forms of the latent trait, one normal and the other negatively skewed. Also, both the known item parameters and estimated item parameters were employed in the simulation study. Descriptive statistics, correlations, scattergrams, accuracy indices, and audit trails were used to compare the different methods of trait estimation in CAT. The results showed that, regardless of the latent trait distribution, MLE and EAP with a normal prior, a uniform prior, or the prior that matches the latent trait distribution using either 20 or 30 quadrature points provided relatively accurate estimation in CAT based on the generalized partial credit model. However, EAP using only 10 quadrature points did not work well in the generalized partial credit CAT. Also, the study found that increasing the number of quadrature points from 20 to 30 did not increase the accuracy of EAP estimation. Therefore, it appears 20 or more quadrature points are sufficient for accurate EAP estimation. The results also showed that EAP with a negatively skewed prior and positively skewed prior performed poorly for the normal data set, and EAP with positively skewed prior did not provide accurate estimates for the negatively skewed data set. Furthermore, trait estimation in CAT using estimated item parameters produced results similar to those obtained using known item parameters. In general, when at least 20 quadrature points are used, EAP estimation with a normal prior, a uniform prior or the prior that matches the latent trait distribution appears to be a good alternative to MLE in the application of polytomous CAT based on the generalized partial credit model. (PsycINFO Database Record (c) 2003 APA, all rights reserved).