  In this paper we follow our previous research in the area of Computerized
Adaptive Testing (CAT). We present three different methods for CAT. One of
them, the item response theory, is a well established method, while the other
two, Bayesian and neural networks, are new in the area of educational testing.
In the first part of this paper, we present the concept of CAT and its
advantages and disadvantages. We collected data from paper tests performed with
grammar school students. We provide the summary of data used for our
experiments in the second part. Next, we present three different model types
for CAT. They are based on the item response theory, Bayesian networks, and
neural networks. The general theory associated with each type is briefly
explained and the utilization of these models for CAT is analyzed. Future
research is outlined in the concluding part of the paper. It shows many
interesting research paths that are important not only for CAT but also for
other areas of artificial intelligence.
