Grammar Practice Item Creation Using Language Models

Automatic content generation by one or more computing devices can include receiving data comprising an original sentence with a grammar artifact of interest. Thereafter, a plurality of distractor candidates are generated based on the original sentence with the grammar artifact of interest. At least one machine learning-based language model then scores each of the distractor candidates. These scores characterize a likelihood of the corresponding distractor candidate being selected as part of an assessment by a subject. The distractor candidates can be filtered to result in a filtered list of distractor candidates from which the x top scoring distractor candidates can be selected. A grammar practice item is then generated based on the original sentence and the x top scoring distractor candidates. The grammar practice can then be provided. Related apparatus, systems, and articles are also described.