How Teachers Can Use Large Language Models and Bloom's Taxonomy to
  Create Educational Quizzes
Sabina Elkins Ekaterina Kochmar Jackie C. K. Cheung Iulian Serban
Question generation (QG) is a natural language processing task with an
abundance of potential benefits and use cases in the educational domain. In
order for this potential to be realized, QG systems must be designed and
validated with pedagogical needs in mind. However, little research has assessed
or designed QG approaches with the input from real teachers or students. This
paper applies a large language model-based QG approach where questions are
generated with learning goals derived from Bloom's taxonomy. The automatically
generated questions are used in multiple experiments designed to assess how
teachers use them in practice. The results demonstrate that teachers prefer to
write quizzes with automatically generated questions, and that such quizzes
have no loss in quality compared to handwritten versions. Further, several
metrics indicate that automatically generated questions can even improve the
quality of the quizzes created, showing the promise for large scale use of QG
in the classroom setting.