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Bridging the Gender Gap: The Role of AI-Powered Math Story Creation in Learning Outcomes
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AI provides strong scaffolds for core practices, markedly improving the quality and conventionality of learner outputs-for example, enhancing grammatical accuracy and argumentative coherence in writing, and supporting problem-solving and experimental design in science and mathematics-thereby helping learners meet baseline practice demands more efficiently.
“First, AI provides strong scaffolds for core practices, markedly improving the quality and conventionality of learner outputs-for example, enhancing grammatical accuracy and argumentative coherence in writing [86,87,145], and supporting problem-solving and experimental design in science and mathematics [77,88,93,103,147]-thereby helping learners meet baseline practice demands more efficiently.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [88]
Citations
The citation lists haven’t been fetched yet
References 23 · Cited by 12
Research works printed but not linked: 5
- Zachary Levonian Chenglu Li Wangda Zhu Anoushka Gade Owen Henkel Millie-Ellen Postle and Wanli Xing. 2023. Retrieval-augmented generation to improve math question-answering: Trade-offs between groundedness and human preference. arXiv preprint arXiv:https://arXiv.org/abs/2310.03184 (2023). · arXiv
- Edgar C Merkle and Yves Rosseel. 2015. blavaan: Bayesian structural equation models via parameter expansion. arXiv preprint arXiv:https://arXiv.org/abs/1511.05604 (2015). · arXiv
- H. Niemi S. Niu M. Vivitsou and B. Li. 2018. Digital storytelling for twenty-first-century competencies with math literacy and student engagement in China and Finland. Contemporary Educational Technology 9 4 (2018) 331–353. 10.30935/cet.471005 · DOI
- David Sadker and Karen R Zittleman. 2009. Still failing at fairness: How gender bias cheats girls and boys in school and what we can do about it. Simon and Schuster.
- D. Statter and M. Armoni. 2020. Teaching abstraction in computer science to 7th grade students. ACM Transactions on Computing Education (TOCE) 20 1 (2020) 1–37. 10.1145/3381882 · DOI
Metadata
- Authors
- Zhu, W., Xing, W., Lyu, B., Li, C., Zhang, F., & Li, H.
- Affiliations
- University of Florida (US)
- University of Utah (US)
- Venue
- Proceedings of the 15th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2025-03-03
- Pages
- 918-923
- OpenAlex
- W4407842387
- Cited by
- 12
- References
- 23
- Citations by year
- 2026: 7 · 2025: 5
- FWCI
- 14.40
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion, cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Gender Studies › Gender and Technology in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- gender gap in mathematics, AI-generated stories, mathematics achievement, educational technology
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