| Sumario: | Young people have rapidly adopted generative artificial intelligence (genAI) technology, yet little is known about how genAI use relates to mental health. This observational study examined associations between genAI use motivations—social-emotional support, task automation, learning/exploration, and dating/sexuality—and internalizing symptoms, as well as sociodemographic differences. U.S. young adults (N = 1003; ages 18–25; 56.3% women, 39.4% men, 4.3% another gender) completed a cross-sectional online survey. Path models tested associations between genAI motivations and mental health, with comparisons by gender, race/ethnicity, and socioeconomic status (SES). Men, Black youth, and higher SES youth used genAI more and for more purposes. Using genAI for social-emotional support was linked to higher depressive (βs = 0.22–0.29; ps < 0.004) and anxiety symptoms (βs = 0.22–0.30; ps < 0.008), whereas using genAI for learning and exploration was associated with lower symptoms of depression (βs = −0.20 to −0.40; ps < 0.008), anxiety (βs = −0.26 to −0.26; ps < 0.003), and loneliness (βs = −0.28 to −0.42; ps < 0.001). Multiple group comparisons revealed gender-specific patterns: among women, task automation and dating/sexuality motivations were related to poorer mental health (βs = 0.12–0.19; ps < 0.05). Among men, social-emotional support motivations predicted greater loneliness (β = 0.33; p <.001). No significant moderation effects were observed by race/ethnicity or SES. Whereas learning/exploration may be adaptive, reliance on genAI for relational support may exacerbate (or reflect pre-existing) internalizing symptoms, with distinct vulnerabilities by gender. Longitudinal research is needed to clarify temporal pathways and inform interventions.
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