  When caregivers ask open--ended questions to motivate dialogue with children,
it facilitates the child's reading comprehension skills.Although there is scope
for use of technological tools, referred here as "intelligent tutoring
systems", to scaffold this process, it is currently unclear whether existing
intelligent systems that generate human--language like questions is beneficial.
Additionally, training data used in the development of these automated question
generation systems is typically sourced without attention to demographics, but
people with different cultural backgrounds may ask different questions. As a
part of a broader project to design an intelligent reading support app for
Latinx children, we crowdsourced questions from Latinx caregivers and
noncaregivers as well as caregivers and noncaregivers from other demographics.
We examine variations in question--asking within this dataset mediated by
individual, cultural, and contextual factors. We then design a system that
automatically extracts templates from this data to generate open--ended
questions that are representative of those asked by Latinx caregivers.
