  Most learners fail to develop deep text comprehension when reading textbooks
passively. Posing questions about what learners have read is a well-established
way of fostering their text comprehension. However, many textbooks lack
self-assessment questions because authoring them is timeconsuming and
expensive. Automatic question generators may alleviate this scarcity by
generating sound pedagogical questions. However, generating questions
automatically poses linguistic and pedagogical challenges. What should we ask?
And, how do we phrase the question automatically? We address those challenges
with an automatic question generator grounded in learning theory. The paper
introduces a novel pedagogically meaningful content selection mechanism to find
question-worthy sentences and answers in arbitrary textbook contents. We
conducted an empirical evaluation study with educational experts, annotating
150 generated questions in six different domains. Results indicate a high
linguistic quality of the generated questions. Furthermore, the evaluation
results imply that the majority of the generated questions inquire central
information related to the given text and may foster text comprehension in
specific learning scenarios.
