Generative Artificial Intelligence Applications Use Among US Youth

Abstract

As generative artificial intelligence (GenAI) tools become increasingly integrated into the daily lives of youth, it is critical to study their usage patterns and potential implications for mental health. While there is evidence of a rapid pace of adoption among adults, rates of GenAI use among youth remains largely undocumented. To characterize GenAI application (app) usage among US youth, including adoption rates and time spent. This cross-sectional study documented digital behavior of US youth extracted from a parental monitoring app. Participants were ages 4 to 17 years and were in families using a commercially available Aura app in the US. No identifying information was collected about the child except year of birth. Data were collected using passive sensing methods from naturalistic smart device use between September 2024 and April 2025. Data were analyzed in May and June 2025. Adoption rates (ie, number of youth ever accessing GenAI apps on their device) and time spent using GenAI (ie, average minutes accessing GenAI apps), measured by age and time period. In a cohort of 6488 participants, nearly 2072 youths (31.9%) used GenAI apps on their device. GenAI use was highest among teens (age 13 to 14 years, 899 of 2139 [42.0%]; age 15 to 17 years, 628 of 1246 [50.4%]), although adoption among preteens (age 10 to 12 years, 484 of 2366 [20.5%]) and school-aged children (age 8 to 9 years, 49 of 522 [9.4%]) was not trivial. GenAI usage was higher after school than at nighttime or during school. Overall, users spent a mean (SD) 2.37 (10.55) and a median (IQR) 0.18 (0.04-0.84) minutes a day using GenAI, yet large variances and skewed distributions suggest that a small subset of youth use GenAI extensively, with the heaviest users accessing GenAI for over 40 minutes a day. In this cross-sectional study, Gen AI app use varied widely among participants, with up to half of adolescents having some use and a small subset engaging in heavy use. Future research must address individual differences in GenAI use to determine impacts on development.

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