Health Psychology Research / HPR / Volume 14 / Issue 1 / DOI: 10.14440/hpr.0377
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RESEARCH ARTICLE

Quality of Generative Artificial Intelligence Use and Adolescent Well-Being: Pathways via Happiness, Meaning, and Resilience in Southwest China

Xianfeng Li1* ,  Xi Zhang2
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1 School of Literature, History and Culture, Leshan Normal University, Leshan, Sichuan 614000, China
2 Yuexi Middle School Sichuan, Liangshan Yi Autonomous Prefecture, Sichuan 616650, China
HPR 2026 , 14(1), e81240073; https://doi.org/10.14440/hpr.0377
Submitted: 13 November 2025 | Revised: 6 February 2026 | Accepted: 24 February 2026 | Published: 31 March 2026
© 2026 by the Author(s). Licensee Health Psychology Research, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Background

Evidence on adolescents’ generative artificial intelligence (GenAI) use and its associations with mental health is mixed.

Objective

We aim to examine whether GenAI-use motivation is associated with psychological resilience, testing both parallel mediation through happiness and meaning and serial mediation via happiness → meaning.

Methods

Adolescents attending boarding high schools in Southwest China with an on-campus phone ban (N = 395) completed measures of GenAI-use motivation, happiness, meaning in life, and resilience. Weekly off-campus/home GenAI-use frequency was self-reported using a single ordinal item. GenAI was defined as content-generating artificial intelligence accessed via standalone tools or embedded features. Partial least squares structural equation modeling with bias-corrected and accelerated bootstrapping (5,000 resamples) estimated associations and indirect effects.

Results

Motivation was weakly associated with happiness (β = 0.128) and positively associated with meaning (β = 0.197); happiness was related to meaning (β = 0.535). The total indirect effect on resilience was β = 0.183 (95% CI = [0.085, 0.277]) via happiness (β = 0.050, 95% CI = [0.001, 0.093]), meaning (β = 0.098, 95% CI = [0.047, 0.156]), and happiness → meaning (β = 0.034, 95% CI = [0.001, 0.068]). Self-reported use frequency showed a weak negative association with resilience (β = −0.062). The model explained 61.3% of the variance in resilience (R2 = 0.613).

Conclusion

Adolescents’ motivation for using GenAI may be more informative than frequency alone. School-based GenAI literacy, supported by educator professional learning that promotes guided, goal-directed use, may help foster adolescents’ meaning in life and psychological resilience.

Keywords
Adolescents
Generative artificial intelligence
Happiness
Meaning in life
Psychological resilience
Funding
This study was funded by the Key Research Base of Humanities and Social Sciences of Sichuan Provincial Higher Education-Sichuan Center for Rural Education Development (SCXCJY2025B01) and the Leshan Normal University Research Project (KYPY2024-0022).
References
  1. McBain RK, Zhang F, Cantor J, et al. Use of generative AI for mental health advice among US adolescents and young adults. JAMA Netw Open. 2025;8(11):e2542281.  doi: 10.1001/jamanetworkopen.2025.42281

 

  1. Karakose T, Tulubas T, Papadakis S. Revealing the intellectual structure and evolution of digital addiction research: an integrated bibliometric and science mapping approach. Int J Environ Res Public Health. 2022;19(22):14883. doi: 10.3390/ijerph192214883

 

  1. Diefenbach S. The potential and challenges of digital well-being interventions: Positive technology research and design in light of the bitter-sweet ambivalence of change. Front Psychol. 2018;9:331. doi: 10.3389/fpsyg.2018.00331

 

  1. Odgers CL, Jensen MR. Annual research review: Adolescent mental health in the digital age— facts, fears, and future directions. J Child Psychol Psychiatry. 2020;61(3):336-348.  doi: 10.1111/jcpp.13190

 

  1. Orben A. Teenagers, screens and social media: A narrative review of reviews and key studies. Soc Psychiatry Psychiatr Epidemiol. 2020;55(4):407-414. doi: 10.1007/s00127-019-01825-4

 

  1. Przybylski AK, Orben A, Weinstein N. How Much Is Too Much? Examining the Relationship Between Digital Screen Engagement and Psychosocial Functioning in a Confirmatory Cohort Study. J Am Acad Child Adolesc Psychiatry. 2020;59(9):1080- 1088. doi: 10.1016/j.jaac.2019.06.017

 

  1. Organisation for Economic Co-operation and Development. How’s Life for Children in the Digital Age? Paris, France: OECD Publishing; 2025. doi: 10.1787/0854b900-en

 

  1. Goodyear VA, Randhawa A, Adab P, et al. School phone policies and their association with mental wellbeing, phone use, and social media use (SMART Schools): a cross-sectional observational study. Lancet Reg Health Eur. 2025;51:101211. doi: 10.1016/j.lanepe.2025.101211

 

  1. Goh AYH, Hartanto A, Majeed NM. Generative artificial intelligence dependency: scale development, validation, and its motivational, behavioral, and  psychological  Comput Hum Behav Rep. 2025;20:100845. doi: 10.1016/j.chbr.2025.100845

 

  1. WHO Regional Office for Europe. Teens, screens and mental health: New WHO report indicates need for healthier online habits among adolescents. Posted 2024. Available from: https://www.who.int/europe/news/item/25-09-2024-teens-screens-and-mental-health [Last accessed on 2025 Oct 19].

 

  1. Ryan RM, Deci EL. Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. New York, NY: Guilford Press; 2017. doi: 10.1521/978.14625/28806

 

  1. Reinecke L, Gilbert A, Eden A. Self-regulation as a key boundary condition in the relationship between social media use and well-being. Curr Opin Psychol. 2022;45:101296. doi: 10.1016/j.copsyc.2021.12.008

 

  1. Valkenburg PM, Peter J. The differential susceptibility to media effects model. J Commun. 2013;63(2):221-243. doi: 10.1111/jcom.12024

 

  1. Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents. Prev Med Rep. 2018;12:271-283. doi: 10.1016/j.pmedr.2018.10.003

 

  1. Miao F, Holmes W. Guidance for Generative AI in Education and Research. Paris, France: UNESCO; 2023. doi: 10.54675/EWZM9535

 

  1. Vanhoffelen G, Vandenbosch L, Schreurs L. Teens, tech, and talk: adolescents’ use of and emotional reactions to Snapchat’s My AI chatbot. Behav Sci. 2025;15(8):1037. doi: 10.3390/bs15081037

 

  1. Hobfoll   Conservation  of  resources: A  new  attempt  at  conceptualizing stress. Am   Psychol.   1989;44(3):513-524. doi: 10.1037/0003-066X.44.3.513

 

  1. Hobfoll SE, Halbesleben J, Neveu J-P, Westman M. Conservation of  resources  in  the organizational context: The reality of resources and their    Annu  Rev  Organ  Psychol Organ  Behav.  2018(1);5:103-128. doi: 10.1146/annurev-orgpsych-032117-104640

 

  1. Fredrickson BL. The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am Psychol. 2001;56(3):218- 226. doi: 10.1037/0003-066X.56.3.218

 

  1. Diener E, Oishi S, Tay L. Advances in subjective well-being research. Nat Hum Behav. 2018;2:253- 260. doi: 10.1038/s41562-018-0307-6

 

  1. King LA, Hicks JA, Krull JL, Del Gaiso AK. Positive affect and the experience of meaning in life. J Pers Soc Psychol. 2006;90(1):179-196. doi: 10.1037/0022-3514.90.1.179

 

  1. Masten AS. Ordinary Magic: Resilience in Development. New York, NY: Guilford Press; 2014.

 

  1. Steger MF, Frazier P, Oishi S, Kaler M. The Meaning in Life Questionnaire: Assessing the presence of and search for meaning in life.J Couns Psychol. 2006;53(1):80-93. doi: 10.1037/0022-0167.53.1.80

 

  1. Sharma G, Yaffe MJ, Ghadiri P, et al. Use of Artificial Intelligence  in  Adolescents’ Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions. JMIR Ment Health. 2025;12:e70438-e70438. doi: 10.2196/70438

 

  1. Zambelli M, Ellena AM, Tagliabue S, Pozzi M, Marta E. The role of resilience in fostering late adolescents’ meaning-making process: a latent pro- file analysis. J Happiness Stud. 2024;25(7):91. doi: 10.1007/s10902-024-00803-1

 

  1. Beaton DE,  Bombardier  C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures.  Spine.  2000;25(24):3186-3191. doi: 10.1097/00007632-200012150-00014

 

  1. Ryan RM,    The  Oxford Handbook of  Self-Determination  Theory.  Oxford, UK: Oxford  University  Press;  2023.  doi: 10.1093/oxfordhb/9780197600047.001.0001

 

  1. Lyubomirsky S, Lepper HS. A measure of subjective happiness: Preliminary reliability and con- struct validation. Soc Indic Res. 1999;46(2):137-155. doi: 10.1023/A:1006824100041

 

  1. Connor KM, Davidson JRT. Development of a new resilience scale: The Connor–Davidson Resilience Scale  (CD-RISC).  Depress Anxiety. 2003;18(2):76-82. doi: 10.1002/da.10113

 

  1. Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, Bernard J. The Brief Resilience Scale: Assessing the ability to bounce back. Int J  Behav  Med.  2008;15(3):194-200. doi: 10.1080/10705500802222972

 

  1. Dijkstra TK, Henseler J. Consistent partial least squares path modeling. MIS Q. 2015;39(2):297-316. doi: 10.25300/MISQ/2015/39.2.02

 

  1. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural  equation  J Acad Mark Sci. 2015;43(1):115-135. doi: 10.1007/s11747-014-0403-8

 

  1. Franke GR, Sarstedt M. Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Res. 2019;29(3):430- 447. doi: 10.1108/IntR-12-2017-0515

 

  1. Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. EBR. 2019;31(1):2-24. doi: 10.1108/ebr-11-2018-0203

 

  1. Sarstedt M, Ringle CM, Hair JF. Partial least squares structural equation modeling. In: Homburg C, Klarmann M, Vomberg A, eds. Handbook of Market Research. Cham, Switzerland: Springer; 2022:587- 632. doi: 10.1007/978-3-319-57413-4_15

 

  1. Hair JF, Sarstedt M, Ringle CM, Sharma PN, Liengaard BD. Going beyond the untold facts in PLS– SEM and moving forward. EJM. 2024;58(13):81-106. doi: 10.1108/ejm-08-2023-0645

 

  1. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39-50. doi: 10.1177/002224378101800104

 

  1. American Psychological Association. Artificial intelligence and adolescent well-being: Health advisory. Available from: https://www.apa.org/topics/artificial-intelligence-machine-learning/health-advisory-ai-adolescent-well-being [Last accessed on 2025 Oct 19].

 

  1. WHO Regional Office for Europe. Addressing the digital determinants of youth mental health: Policy brief. Posted 2025. Available from: https://www.who.int/europe/publications/i/item/WHO- EURO-2025-12187-51959-79685 [Last accessed on 2025 Oct 19].

 

  1. Perlmutter E, Dwyer B, Torous J. Social media and youth mental health: Assessing the impact through current and novel digital phenotyping methods. Curr Treat Options Psychiatry. 2024;11(2):34-51. doi: 10.1007/s40501-024-00312-1
Conflict of interest
The authors declare no competing interests.
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Health Psychology Research, Electronic ISSN: 2420-8124 Published by Health Psychology Research
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