Computer adaptive testing offers many important benefits to support and promote life-long learning. Computers permit testing on-demand thereby allowing students to take the test at any time during instruction; items on computerized tests are scored immediately thereby providing students with instant feedback; computerized tests permit continuous administration thereby allowing students to have more choice about when they write their exams. But despite these important benefits, the advent of computer adaptive testing has also raised formidable challenges, particularly in the area of item development. Educators must have access to large numbers of diverse, high-quality test items to implement computerize adaptive testing because items are continuously administered to students. Hence, hundreds or even thousands of items are needed to develop the test item banks necessary for computer adaptive testing. Unfortunately, educational test items, as they are currently created, are time consuming and expensive to develop because each individual item is written, initially, by a content specialist and, then, reviewed, edited, and revised by groups of content specialists to ensure the items yield reliable and valid information. Hence, item development is one of the most important problems that must be solved before we can migrate to computer adaptive testing to support life-long learning because large numbers of high-quality, content-specific, test items are required.

One promising item development method that may be used to address this challenge is with automatic item generation. Automatic item generation is a relatively new but rapidly evolving research area where cognitive and psychometric modelling practices are used produce hundreds of new test items with the aid of computer technology. The purpose of our presentation is to describe a new methodology for generating both the items and the rationales required to solve each generated item in order to produce the feedback needed to support life-long learning. Our item generation methodology will first be described. To ensure our description is practical, the method will also be demonstrated using generated items from the health sciences to demonstrate how item generation can promote life-long learning for medical educators and practitioners.

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