Improving item pool utilization for health professions examinations under variable-length computerized adaptive testing designs: a shadow-test approach

The shadow-test approach to computerized adaptive testing (CAT) ensures content validity in health professions examinations but may suffer from poor item pool utilization in variable-length designs, increasing operational costs and security risks. This study aimed to address this challenge by developing algorithms that enhance the sustainability of shadow CAT in variable-length design. A simulation study was conducted to evaluate 3 proposed modifications of the a-stratification method designed to improve item pool utilization. These methods, which integrated randomesque selection and multiple-form strategies, were compared with 2 baseline algorithms within a variable-length shadow CAT framework. Performance was assessed in terms of measurement precision, pool utilization, and test efficiency.