Generative Artificial Intelligence in the Lives of Young Adults: Exploring Motivations and Mental Health.
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...
| Publicado en: | Journal of Adolescent Health Vol. 79; no. 2; pp. 218 - 226 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194733346&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194733346 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1054139X ILK jtl: Journal of Adolescent Health issn: 1054139X maglogo: N pubinfo: dt: Aug2026 vid: 79 iid: 2 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 194733346 194733346 194733346 10.1016/j.jadohealth.2026.03.014 194733346 ppf: 218 ppct: 8 formats: tig: atl: Generative Artificial Intelligence in the Lives of Young Adults: Exploring Motivations and Mental Health. aug: au: Maheux, Anne J. Maes, Chelly Buck, Benjamin affil: Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina sug: subj: Artificial Intelligence, Generative Utilization Artificial Intelligence, Generative Utilization Motivation Evaluation Mental Disorders Symptoms Support, Psychosocial Dating Sexuality Internalizing Behavior Sociodemographic Factors Human Adolescence Adult Male Female United States Nonexperimental Studies Cross Sectional Studies Descriptive Statistics Models, Theoretical Sex Factors Race Factors Ethnic Groups Social Class Depression Anxiety Loneliness Mental Health Comparative Studies Automation Task Performance and Analysis Adolescent: 13-18 years Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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