Automatic item generation unleashed: an evaluation of a large-scale deployment of item models

Abstract

Automatic item generation represents a potential solution to the increased item development demands in this era of continuous testing. However, the use of test items that are automatically generated on-the-fly poses significant psychometric challenges for item calibration. The solution that has been suggested by a small but growing number of authors is to replace item calibration with item model (or family) calibration and to adopt a multilevel approach where items are nested within item models. Past research on the feasibility of this approach was limited to simulations or small-scale illustrations of its potential. The purpose of this study was to evaluate the results of a large-scale deployment of automatic item generation in a low-stakes adaptive testing context, with a large number of item models, and a very large number of randomly generated item instances.