Martin C. Yu
Abstract
Mass production of test items involves numerous steps and takes time. Technology can play a key role in supplementing human resources whether gathering and storing source materials, communicating with subject matter experts, or synchronizing and coordinating activities during a complex or fast-paced development cycle. Our work in automated item generation (AIG) using natural language processing is one example of this process unfolding in practice. Over the past few years, there has been a surge in developments in the fields of natural language understanding and generation (NLU/NLG) regarding applications of language models developed via machine learning techniques that have yet to be applied to the area of AIG. We introduce NLU/NLG approaches to AIG and describe our efforts in making the technology accessible to the broader test development community.
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