Under a multidimensional item response theory (MIRT) computerized adaptive testing (CAT) testing scenario, a trait estimate (θ) in one dimension will provide clues for subsequently seeking a solution in other dimensions. This feature may enhance the efficiency of MIRT CAT’s item selection and its scoring algorithms compared with its counterpart, the unidimensional CAT (UCAT). The present study used existing Reading and Math test data to generate simulated item parameters. A confirmatory item factor analysis model was applied to the data using NOHARM to produce interpretable MIRT item parameters. Results showed that MIRT CAT, conditional on the constraints, was quite capable of producing accurate estimates on both measures. Compared with UCAT, MIRT CAT slightly increased the accuracy of both trait estimates, especially for the low-level or high-level trait examinees in both measures, and reduced the rate of unused items in the item pool.