Date
Publisher
arXiv
This study examined how high school students with different motivational profiles use generative AI tools in math and writing. Through K-means clustering analysis of survey data from 6,793 Mexican high school students, we identified three distinct motivational profiles based on self-concept and perceived subject value. Results revealed distinct domain-specific AI usage patterns across students with different motivational profiles. Our findings challenge one-size-fits-all AI integration approaches and advocate for motivationally-informed educational interventions.
Study at a glance
- Question: How do high school students with different motivation levels use generative AI tools in math and writing?
- Setting and data: 6,793 Mexican high school students.
- Method: Survey analyzed by grouping students into profiles (K-means clustering analysis).
Why it matters
One-size-fits-all approaches to generative AI integration do not account for student motivation. Addressing this requires examining how high school students with different motivational profiles use generative AI tools in math and writing.
Key findings
- Among 6,793 Mexican high school students, generative AI tool use in math and writing was linked to students' motivational profiles.
- Students' generative AI usage patterns were domain-specific, differing between math and writing.
- Students exhibited three distinct motivational profiles based on their self-concept and how much they valued each subject.
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?
Why use AI?
Study design
