Evaluating the Psychometric Characteristics of Generated Multiple-Choice Test Items

Item development is a time-and resource-intensive process. Automatic item generation integrates cognitive modeling with computer technology to systematically generate test items. To date, however, items generated using cognitive modeling procedures have received limited use in operational testing situations. As a result, the psychometric characteristics of generated multiple-choice test items are largely unknown and undocumented. We present item analysis results from one of the first empirical studies designed to evaluate the psychometric properties of generated multiple-choice items using the results from a high stakes national medical licensure examination. The item analysis results for the correct option revealed that the generated items measured examinees' performance across a broad range of ability levels while, at the same time, providing a consistently strong level of discrimination for each item. Results for the incorrect options revealed that the generated items consistently differentiated the low from the high performing examinees.