A computerized classification test (CCT) refers to a Performance Appraisal System that is administered by computer for the purpose of classifying examinees.
This process repeats until the examinee is classified or another ending point is satisfied (all items in the bank have been administered, or a maximum test length is reached).
IRT, on the other hand, assumes a trait model; the knowledge or ability measured by the test is a continuum.
The classification groups will need to be more or less arbitrarily defined along the continuum, such as the use of a cutscore to demarcate masters and non-masters, but the specification of item parameters assumes a trait model.
More importantly, CTT requires fewer examinees in the sample for calibration of item parameters to be used eventually in the design of the CCT, making it useful for smaller testing programs.
IRT offers greater specificity, but the most important reason may be that the design of a CCT (and a CAT) is expensive, and is therefore more likely done by a large testing program with extensive resources.
Estimate-based methods (also known as adaptive selection) maximize information at the current estimate of examinee ability, regardless of the location of the cutscore.
Because the confidence interval termination criterion is centered around the examinees ability estimate, estimate-based item selection is more appropriate.
Bayesian decision theory methods offer great flexibility by presenting an infinite choice of loss/utility structures and evaluation considerations, but also introduce greater arbitrariness.