Generative AI and Youth Learning Across the Globe

One major concern people have is the possibility that AI might take away the “productive struggle” necessary for learning. There is a fear that youth may miss out on opportunities to develop the foundational skills and knowledge they need to thrive academically. Yet, one often overlooked point in this narrative is the assumption that AI created this problem. In reality, factors like motivation, readiness, and self-regulation have always played a role in learning–even before AI entered the picture. To truly understand AI’s impact, we need to look deeper into the factors that shape the ways AI is used and the consequences these patterns have on learning.

In this series of studies, we set out to explore this through a two-pronged approach: first, by describing the phenomenon—how adolescents are using AI in their learning processes; and second, by understanding the antecedents—the individual, social, and contextual factors that shape how and why students choose to engage with AI in the first place.

Ongoing Studies

• Large scale survey in Latin America. We collaborated on the design of a large-scale survey, which was distributed by ministries of education and language centers in Mexico, Colombia, and Panama. The survey includes both structured and open-ended questions, capturing students’ access to AI, frequency and purpose of use, emotional responses, and reflections on how AI affects their self-perception and academic identity. We ask questions including:

  • How often and in what ways students use AI tools—for outlining essays, solving math problems, studying for tests, and more

  • How AI use influences students’ beliefs about their own academic abilities and performance

  • Patterns of dependency or trust in AI, including concerns about overreliance

  • Students’ own reflections on how AI is changing the way they learn, study, and think about school

• AI’s impact on academic performance.In this study, we aim to uncover causal links between AI usage and academic performance—questions that were difficult to address through the surveys above. We seek to understand a set of factors, including motivation, beliefs, academic readiness, self-regulation, and AI literacy, that may influence whether youth engage with AI as a means of outsourcing or as an additional resource for deeper learning. We have been collaborating with high school students, educators, and district leaders to co-design the study and generate evidence that may support your decision-making. Contact us if you would like to be part of it!