Kayce Mastrup
California State University-Monterey Bay, CA·Education Research
Children increasingly have access to artificial intelligence (AI) chatbots while doing homework, but this use is largely unstructured. Elementary students may not yet be able to judge whether AI-generated answers are correct.
- Fields
Education Research
Computer Science
- Topics
AI literacy
artificial intelligence in education
large language models
- Grants
- 1 known grant, last ended Oct 2026
What they work on
- Prototype a bounded large language model homework tool for grades 3–5 that uses structured prompting constraints to guide students' reasoning on science and math problems rather than giving direct answers.
- Design child-appropriate explanation features and lesson materials that help students and caregivers critically interpret AI-generated output.
- Develop and pilot AI literacy lesson plans and family-facing supports that prepare children and parents to use the tool productively and ethically.
- Produce a conceptual framework for aligning AI tools with afterschool and informal learning settings that can be scaled across programs.
More about the research
This project asks whether a carefully limited AI tool, paired with lessons on AI literacy (understanding how AI works and when to trust it), can help upper elementary students and their families work through science and math homework together.
Why it matters
Many families struggle to help children with science and math homework, and children are meeting AI tools without the skills to judge them. A guided AI homework tool with matching AI literacy lessons could help close comprehension gaps, strengthen links between home and school learning, and give afterschool programs a model for responsible AI use with young students.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Maciej Pankiewicz
University of Pennsylvania, PA·Education Research
Extend the Decimal Point mathematics learning game so that a large language model generates hints personalized to each student's problem-solving history, misconceptions and in-game learning behaviors.
Fred Martin
University of Texas at San Antonio, TX·Education Research
Document current understandings, policies and practices for using and teaching AI literacy in partner middle school districts in Texas and New York, working with teachers and technology coaches.
Molly Fisher
Rowan University, NJ·Education Research
Test whether generative AI tools given rubrics and exemplars can score teachers' professional noticing responses in line with expert human raters, across the components of attending, interpreting and deciding.
The school
California State University-Monterey Bay: 1 labs on PI Pages, $12M a year on research.
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