  This article describes new results of an application using transformer-based
language models to automated item generation (AIG), an area of ongoing interest
in the domain of certification testing as well as in educational measurement
and psychological testing. OpenAI's gpt2 pre-trained 345M parameter language
model was retrained using the public domain text mining set of PubMed articles
and subsequently used to generate item stems (case vignettes) as well as
distractor proposals for multiple-choice items. This case study shows promise
and produces draft text that can be used by human item writers as input for
authoring. Future experiments with more recent transformer models (such as
Grover, TransformerXL) using existing item pools are expected to improve
results further and to facilitate the development of assessment materials.
