projectCAT: A Shiny Application for Computerized Adaptive Testing with Sympson-Hetter Exposure Control, Content Balancing, and Item Selection Method Comparison.

projectCAT is an open-source R Shiny application providing a complete, code-free computerized adaptive testing (CAT) simulation environment. It integrates IRT calibration (1PL, 2PL, 3PL via marginal maximum likelihood), three adaptive item selection methods, namely, Maximum Fisher Information (MFI), Mean Expected Information (MEI), and Minimum Expected Posterior Variance (MEPV), three ability estimation methods (EAP, ML, and MAP), Sympson-Hetter (SH) exposure control, and content balancing with user-specified target proportions across multiple domains. Both SH and content balancing operate identically for single-examinee and full multi-examinee simulations. Applied to the built-in 300-item 2PL demo bank ( n = 500 , 20 items, MFI + EAP), RMSE = . 36 , correlation = . 94 , and item overexposure was reduced to 11.3% under SH ( r max = . 20 ) . Content proportions matched a 50/30/20 user-specified split exactly. projectCAT is freely available at https://hdmeasurement.shinyapps.io/projectCAT/.