  Controllable text generation (CTG) by large language models has a huge
potential to transform education for teachers and students alike. Specifically,
high quality and diverse question generation can dramatically reduce the load
on teachers and improve the quality of their educational content. Recent work
in this domain has made progress with generation, but fails to show that real
teachers judge the generated questions as sufficiently useful for the classroom
setting; or if instead the questions have errors and/or pedagogically unhelpful
content. We conduct a human evaluation with teachers to assess the quality and
usefulness of outputs from combining CTG and question taxonomies (Bloom's and a
difficulty taxonomy). The results demonstrate that the questions generated are
high quality and sufficiently useful, showing their promise for widespread use
in the classroom setting.
