Improving Ability Estimation Accuracy for Automated Item Generated Forms under Multistage Testing

Abstract The emergence of automated item generation (AIG) techniques has intensified discussions around their application in assessment development. Some testing companies have already begun developing software to construct exams using AIG. However, the current literature offers limited insights into the characteristics of items generated through AIG, particularly in the realm of multistage testing (MST). This study proposes a novel approach for adjusting template item parameters to enhance ability estimation accuracy under the MST context. A simulation study was conducted using two MST designs with varying numbers of stages and modules. Results demonstrated that the proposed method significantly improved the accuracy of person parameter estimates compared to a more practical, yet less precise, approach that assumes all item clones share identical parameters.