Young people, learning, and generative AI: A rapid literature review and implications for PreK-12 education
Natasha Arthars, Hongzhi Yang, Matthew Hill, Danny Liu, Lina Markauskaitė
Generative artificial intelligence (GenAI) is rapidly becoming part of the environments in which young people learn, live, and grow. This rapid literature review synthesises available research results on when, how, why, and under what conditions GenAI may support or undermine learning in PreK-12 education. The review is undertaken from a perspective of the learning sciences and is informed by an ecological view of learning. It foregrounds three interdependent dimensions: the characteristics and capacities of learners; the contexts in which learning takes place; and the cultural aspects that shape what learning means, how it is valued, and who participates. This report is based on a rapid literature review methodology. It reviewed 271 empirical studies. The results show varied outcomes. The most consistent evidence concerns affective responses, product quality, task completion, and knowledge or skills assessed during or shortly after GenAI-supported activity. Evidence about GenAI’s impact on durable learning, transfer, higher-order reasoning, and self-regulation is less consistent. Students’ learning processes with GenAI and their outcomes are shaped by multiple interacting factors. Learner characteristics, such as prior knowledge, self-regulation, self-efficacy, help-seeking dispositions, trust, and developmental stage, influence how students use GenAI. The design of GenAI tools, in turn, shapes which developmentally important learning processes are supported or bypassed, while task design impacts what learning-relevant work becomes the student’s responsibility. Finally, social arrangements shape how human support and GenAI support interact, and how cultural and institutional conditions frame expectations about knowledge, authority, effort, and learning. The key implication is that decisions about GenAI should focus not only on whether GenAI is used, but also on how all other conditions are configured and how they interact.