Bayesian adaptive assessment with distribution-aware item selection: An empirical study on uncertainty reduction and test efficiency
. Computerized adaptive testing (CAT) combines ability estimation with an item-selection rule that usually favors the currently most informative item. Although randomization is often used for exposure control, security, or robustness, the probability distribution used for item sampling is rarely treated as an explicit design variable. This paper studies this choice in a Bayesian adaptive-assessment framework in which posterior updating is fixed, while the next item is sampled from a distribution over information-ranked candidates. Five kernels are compared: uniform, binomial, normal, exponential, and Poisson. Using interaction logs from 33 test sessions completed by 18 participants, we analyze observed session-level accuracy, test length, early stopping, and posterior uncertainty reduction. The results indicate that the selection kernel affects operational behavior: concentrated kernels tend to produce more stable accuracy and reduce interaction variability, whereas flatter kernels increase exploration and may prolong sessions. The study contributes a compact system-level formulation of distribution-aware item selection and shows how the exploration–exploitation trade-off appears in deployable Bayesian CAT systems.