Optimal test-design methods are applied to rule-based item generation. Three different cases of automated test design are presented: (a) test assembly from a pool of pregenerated, calibrated items; (b) test generation on the fly from a pool of calibrated item families; and (c) test generation on the fly directly from calibrated features defining the item families. The last two cases do not assume any item calibration under a regular response theory model; instead, entire item families or critical features of them are assumed to be calibrated using a hierarchical response model developed for rule-based item generation. The test-design models maximize an expected version of the Fisher information in the test and control critical attributes of the test forms through explicit constraints. Results from a study with simulated response data highlight both the effects of within-family item-parameter variability and the severity of the constraint sets in the test-design models on their optimal solutions.
Geerlings, H., van der Linden, W. J., & Glas, C. A. W. (2013). Optimal test design with rule-based item generation. Applied psychological measurement, 37(2), 140-161. https://doi.org/10.1177/0146621612468313