Comparison of Artificial Intelligence-Assisted Adaptive Testing Methods in Terms of Advantages and Disadvantages
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Keywords:
Item Response Theory, Adaptive Test, Traditional Test, Knowledge Space Theory, Rasch Model, 2PL Model, 3PL ModelAbstract
Adaptive tests aim to measure individuals' ability levels and knowledge levels on a subject in
the shortest and most accurate way. Unlike classical tests, adaptive tests measure the participant's
knowledge level in a more accurate and short way by creating the test according to the participant's ability
level, instead of asking all the questions to each participant. In this text, adaptive test creation with Item
Response Theory (IRT) and Knowledge Space Theory (KST)), which are important in the field of
measurement, evaluation and teaching are discussed. The model structures of these theorems, their roles in
measurement, evaluation and teaching processes, their purposes of use, and their differences with each
other have been evaluated. In addition, one parameter logistic model (Rasch Model), two parameter logistic
Model (2PL), 3 parameter logistic Model (3PL), which are the most well-known models of Item Response
Theory, are discussed and the advantages and disadvantages of each model compared to the other model
are explained. As a result of relevant research, it is shown that all models offer different approaches and
make different contributions to the fields of measurement, evaluation and teaching.
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