How Motivation Relates to Generative AI Use: A Large-Scale Survey of Mexican High School Students

2026-02-26

AI Summary

This study investigates how distinct motivational profiles among high school students influence their use of generative artificial intelligence (AI) in mathematics and writing. Drawing on survey data from 6,793 Mexican adolescents, the authors employed K-means clustering to analyze self-concept and perceived subject value, identifying three distinct motivational types. These profiles exhibited significantly different patterns of generative AI usage across the two disciplines. As the first integration of motivation psychology with research on generative AI in educational contexts, this work argues against a one-size-fits-all approach to AI implementation and instead advocates for instructional strategies tailored to students’ motivational characteristics. The findings provide empirical evidence and methodological support for designing personalized AI-based educational interventions that account for individual differences in academic motivation.

Abstract

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.

Problem

• motivation
• generative AI
• high school students
• AI usage
• educational technology

Innovation

• motivational profiles
• generative AI use
• K-means clustering
• domain-specific patterns
• educational intervention