  In order to train children's ability to ask curiosity-driven questions,
previous research has explored designing specific exercises relying on
providing semantic and linguistic cues to help formulate such questions. But
despite showing pedagogical efficiency, this method is still limited as it
relies on generating the said cues by hand, which can be a very costly process.
In this context, we propose to leverage advances in the natural language
processing field (NLP) and investigate the efficiency of using a large language
model (LLM) for automating the production of the pedagogical content of a
curious question-asking (QA) training. We study generating the said content
using the "prompt-based" method that consists of explaining the task to the LLM
in natural text. We evaluate the output using human experts annotations and
comparisons with hand-generated content. Results suggested indeed the relevance
and usefulness of this content. We also conduct a field study in primary school
(75 children aged 9-10), where we evaluate children's QA performance when
having this training. We compare 3 types of content : 1) hand-generated content
that proposes "closed" cues leading to predefined questions; 2) GPT-3-generated
content that proposes the same type of cues; 3) GPT-3-generated content that
proposes "open" cues leading to several possible questions. We see a similar QA
performance between the two "closed" trainings (showing the scalability of the
approach using GPT-3), and a better one for participants with the "open"
training. These results suggest the efficiency of using LLMs to support
children in generating more curious questions, using a natural language
prompting approach that affords usability by teachers and other users not
specialists of AI techniques. Furthermore, results also show that open-ended
content may be more suitable for training curious question-asking skills.
