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Question generation (QG) is a natural language processing task with anabundance of potential benefits and use cases in the educational domain. Inorder for this potential to be realized, QG systems must be designed andvalidated with pedagogical needs in mind. However, little research has assessedor designed QG approaches with the input from real teachers or students. Thispaper applies a large language model-based QG approach where questions aregenerated with learning goals derived from Bloom's taxonomy. The automaticallygenerated questions are used in multiple experiments designed to assess howteachers use them in practice. The results demonstrate that teachers prefer towrite quizzes with automatically generated questions, and that such quizzeshave no loss in quality compared to handwritten versions. Further, severalmetrics indicate that automatically generated questions can even improve thequality of the quizzes created, showing the promise for large scale use of QGin the classroom setting.

How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes