Date
Publisher
arXiv
Mathematical language is a cornerstone of a child's mathematical development,
and children can effectively acquire this language through storytelling with a
knowledgeable and engaging partner. In this study, we leverage the recent
advances in large language models to conduct free-form, creative conversations
with children. Consequently, we developed Mathemyths, a joint storytelling
agent that takes turns co-creating stories with children while integrating
mathematical terms into the evolving narrative. This paper details our
development process, illustrating how prompt-engineering can optimize LLMs for
educational contexts. Through a user study involving 35 children aged 4-8
years, our results suggest that when children interacted with Mathemyths, their
learning of mathematical language was comparable to those who co-created
stories with a human partner. However, we observed differences in how children
engaged with co-creation partners of different natures. Overall, we believe
that LLM applications, like Mathemyths, offer children a unique conversational
experience pertaining to focused learning objectives.
Study at a glance
- Question: Can an AI storytelling partner help young children learn mathematical language as effectively as a human partner?
- Setting and data: 35 children aged 4–8 years
- Method: User study comparing children co-creating stories with an AI agent versus a human partner
Why it matters
Mathematical language is a cornerstone of a child's mathematical development. Children can effectively acquire this language through storytelling with a knowledgeable and engaging partner.
Key findings
- For 35 children aged 4 to 8, results suggest learning mathematical language with an AI storytelling partner was comparable to learning with a human.
- Children showed differences in how they engaged with an AI storytelling partner compared to a human partner.
- The AI tool can take turns co-creating stories with children while integrating targeted mathematical terms into the evolving narrative.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Study design
