Using Automatic Item Generation to Create Multiple-Choice Questions for Pharmacy Assessment

Abstract

Objective: Automatic item generation (AIG) is a new area of assessment research where a set of multiple-choice questions (MCQs) are created using models and computer technology. Although successfully demonstrated in medicine and dentistry, AIG has not been implemented in pharmacy. The objective was to implement AIG to create a set of MCQs appropriate for inclusion in a summative, high-stakes, pharmacy examination.

Methods: A 3-step process, well evidenced in AIG research, was employed to create the pharmacy MCQs. The first step was developing a cognitive model based on content within the examination blueprint. Second, an item model was developed based on the cognitive model. A process of systematic distractor generation was also incorporated to optimize distractor plausibility. Third, we used computer technology to assemble a set of test items based on the cognitive and item models. A sample of generated items was assessed for quality against Gierl and Lai's 8 guidelines of item quality.

Results: More than 15,000 MCQs were generated to measure knowledge and skill of patient assessment and treatment of nausea and/or vomiting within the scope of clinical pharmacy. A sample of generated items satisfies the requirements of content-related validity and quality after substantive review.

Conclusion: This research demonstrates the AIG process is a viable strategy for creating a test item bank to provide MCQs appropriate for inclusion in a pharmacy licensing examination.

Keywords: automatic item generation; multiple-choice questions; pharmacy assessment; test development.