This study explores the current state of family mental health awareness among adolescents and its promotion pathways, proposing optimization strategies by integrating artificial intelligence (AI) technology. Based on literature analysis, the research highlights the growing severity of adolescent mental health issues and the critical role of family factors in prevention and intervention. However, existing studies primarily focus on psychological perspectives, lacking economic and management viewpoints, while AI applications mainly target post-hoc interventions rather than daily prevention. Using a mixed-methods design, the study developed the Family Adolescent Mental Health Awareness Questionnaire covering three dimensions: emotional management, behavioral support, and social relationships. Exploratory and confirmatory factor analyses validated its reliability and validity. An empirical survey of 295 parents in Beijing revealed that most possess basic awareness in emotional management and behavioral support but struggle with complex emotion recognition and sudden emotion coping. Notably, 30% of parents did not consider seeking professional help for adolescent depressive symptoms. Gender difference analysis showed significantly higher support willingness among female parents. The research further proposes a three-stage AI application framework: 1) privacy-protected multi-source data collection via blockchain encryption; 2) development of natural language processing-based intelligent chatbots for personalized mental health support; 3) establishment of an online education and early-warning system integrating families, schools, and governments to strengthen early intervention. The marginal contribution lies in optimizing mental health service systems through digital technology, providing data-driven policy recommendations for policymakers, and offering actionable solutions for family awareness improvement. Future research should expand sample geographical representation and explore technological adaptability across diverse scenarios.
| Published in | Social Sciences (Volume 14, Issue 3) |
| DOI | 10.11648/j.ss.20251403.12 |
| Page(s) | 202-212 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Adolescent Families, Mental Health, Artificial Intelligence, Questionnaire Scale
| [1] | Wen, F. (2021). Family environment and adolescent mental health in China. China Journal of Social Work, 14(3), 293-307. |
| [2] | Li, X., et al. (2024). The effect of cumulative risk related to family adversity on social adjustment among left-behind children in China. Child Development, 95(3), 1012-1025. |
| [3] | Ahmed, Ragaa. (2022). Assessment of Parents' Awareness regarding their Children Mental Health Status in Eastern Region-KSA. 44-51. |
| [4] | Kuhlman K R, Chiang J J, Bower J E, et al. Persistent low positive affect and sleep disturbance across adolescence moderate link between stress and depressive symptoms in early adulthood [J]. Research on Child and Adolescent Psychopathology, 2020, 48: 109-121. |
| [5] | Miyahara T, Kusaka H, Shimizu D. Interventions for Social Cognition Following Traumatic Brain Injury: A Single-Case Experimental Design [J]. International Journal of Psychology & Psychological Therapy, 2024, 24(3). |
| [6] | Rutter, M., et al. (2021). Parenting styles and adolescent mental health: A meta-analysis. Journal of Child Psychology and Psychiatry, 62(8), 987-1005. |
| [7] | Amato, P. R. (2022). The consequences of divorce for adults and children. Journal of Marriage and Family, 84(1), 35-55. |
| [8] | Isaac, I, Bejar. A Generative Approach to Psychological and Educational Measurement [J]. ETS Research Report Series, 1991, 1991(1): i-54. |
| [9] | Babble, E. (2009). Social research methods (11th ed.). Translated by Qiu Zeqi. Huaxia Publishing House. |
| [10] | Liden, R. C., & Maslyn, J. M. (1998). Multidimensionality of leader-member exchange: An empirical assessment through scale development. Journal of Management, 24(1), 43-72. |
| [11] | Hinkin, T. R. (2005). A brief tutorial on the development of measures for use in survey research. Organizational Research Methods, 8(2), 186-206. |
| [12] | Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage Publications. |
| [13] | Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill Education. |
| [14] | Pang Hongwei, Wang Cuifang, Li Gang, et al. Construction of a Student Mental Health Monitoring and Evaluation System Based on Artificial Intelligence [J]. Educational Measurement and Evaluation, 2022, (03): 31-39. |
| [15] | Xishu, A. F., et al. (2022). Application of Artificial Intelligence in Mental Health and Mental Illnesses. Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences, 506-511. |
| [16] | Van Heerden, A. C., et al. (2023). Global Mental Health Services and the Impact of Artificial Intelligence-Powered Large Language Models. JAMA Psychiatry, 80(7), 662-664. |
| [17] | Li, H., et al. (2024). Systematic Review and Meta-Analysis of AI-Based Conversational Agents for Promoting Mental Health and Well-Being. npj Digital Medicine, 7(1), 1-10. |
| [18] | Zapf, H., et al. (2023). A systematic review of the association between parent-child communication and adolescent mental health. JCPP Advances, 5(4), 12205. |
| [19] | Ju, Q., et al. (2025). Screening Social Anxiety with the Social Artificial Intelligence Picture System Journal of Anxiety Disorders, 109, 102955. |
APA Style
Zhiqi, Z., Shuaina, C., Tao, W., Ennan, Z., Ke, G., et al. (2025). Exploring Families' Perception of Adolescent Mental Health: Using AI to Strengthen Government Social Risk Resilience. Social Sciences, 14(3), 202-212. https://doi.org/10.11648/j.ss.20251403.12
ACS Style
Zhiqi, Z.; Shuaina, C.; Tao, W.; Ennan, Z.; Ke, G., et al. Exploring Families' Perception of Adolescent Mental Health: Using AI to Strengthen Government Social Risk Resilience. Soc. Sci. 2025, 14(3), 202-212. doi: 10.11648/j.ss.20251403.12
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Download@article{10.11648/j.ss.20251403.12,
author = {Zhu Zhiqi and Chi Shuaina and Wang Tao and Zhao Ennan and Gao Ke and Yang Runhan},
title = {Exploring Families' Perception of Adolescent Mental Health: Using AI to Strengthen Government Social Risk Resilience
},
journal = {Social Sciences},
volume = {14},
number = {3},
pages = {202-212},
doi = {10.11648/j.ss.20251403.12},
url = {https://doi.org/10.11648/j.ss.20251403.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ss.20251403.12},
abstract = {This study explores the current state of family mental health awareness among adolescents and its promotion pathways, proposing optimization strategies by integrating artificial intelligence (AI) technology. Based on literature analysis, the research highlights the growing severity of adolescent mental health issues and the critical role of family factors in prevention and intervention. However, existing studies primarily focus on psychological perspectives, lacking economic and management viewpoints, while AI applications mainly target post-hoc interventions rather than daily prevention. Using a mixed-methods design, the study developed the Family Adolescent Mental Health Awareness Questionnaire covering three dimensions: emotional management, behavioral support, and social relationships. Exploratory and confirmatory factor analyses validated its reliability and validity. An empirical survey of 295 parents in Beijing revealed that most possess basic awareness in emotional management and behavioral support but struggle with complex emotion recognition and sudden emotion coping. Notably, 30% of parents did not consider seeking professional help for adolescent depressive symptoms. Gender difference analysis showed significantly higher support willingness among female parents. The research further proposes a three-stage AI application framework: 1) privacy-protected multi-source data collection via blockchain encryption; 2) development of natural language processing-based intelligent chatbots for personalized mental health support; 3) establishment of an online education and early-warning system integrating families, schools, and governments to strengthen early intervention. The marginal contribution lies in optimizing mental health service systems through digital technology, providing data-driven policy recommendations for policymakers, and offering actionable solutions for family awareness improvement. Future research should expand sample geographical representation and explore technological adaptability across diverse scenarios.
},
year = {2025}
}
TY - JOUR T1 - Exploring Families' Perception of Adolescent Mental Health: Using AI to Strengthen Government Social Risk Resilience AU - Zhu Zhiqi AU - Chi Shuaina AU - Wang Tao AU - Zhao Ennan AU - Gao Ke AU - Yang Runhan Y1 - 2025/05/14 PY - 2025 N1 - https://doi.org/10.11648/j.ss.20251403.12 DO - 10.11648/j.ss.20251403.12 T2 - Social Sciences JF - Social Sciences JO - Social Sciences SP - 202 EP - 212 PB - Science Publishing Group SN - 2326-988X UR - https://doi.org/10.11648/j.ss.20251403.12 AB - This study explores the current state of family mental health awareness among adolescents and its promotion pathways, proposing optimization strategies by integrating artificial intelligence (AI) technology. Based on literature analysis, the research highlights the growing severity of adolescent mental health issues and the critical role of family factors in prevention and intervention. However, existing studies primarily focus on psychological perspectives, lacking economic and management viewpoints, while AI applications mainly target post-hoc interventions rather than daily prevention. Using a mixed-methods design, the study developed the Family Adolescent Mental Health Awareness Questionnaire covering three dimensions: emotional management, behavioral support, and social relationships. Exploratory and confirmatory factor analyses validated its reliability and validity. An empirical survey of 295 parents in Beijing revealed that most possess basic awareness in emotional management and behavioral support but struggle with complex emotion recognition and sudden emotion coping. Notably, 30% of parents did not consider seeking professional help for adolescent depressive symptoms. Gender difference analysis showed significantly higher support willingness among female parents. The research further proposes a three-stage AI application framework: 1) privacy-protected multi-source data collection via blockchain encryption; 2) development of natural language processing-based intelligent chatbots for personalized mental health support; 3) establishment of an online education and early-warning system integrating families, schools, and governments to strengthen early intervention. The marginal contribution lies in optimizing mental health service systems through digital technology, providing data-driven policy recommendations for policymakers, and offering actionable solutions for family awareness improvement. Future research should expand sample geographical representation and explore technological adaptability across diverse scenarios. VL - 14 IS - 3 ER -
School of Finance, Tianjin University of Finance and Economics, Tianjin, China
Guanghua School of Management, Peking University, Beijing, China
School of Finance and Taxation, Capital University of Economics and Business, Beijing, China
School of Professional Studies, Columbia University, New York, America
Shandong Agricultural Development Credit Guarantee Co., Ltd, Jinan, China
Daoyuan Campus of Beijing No. 4 High School, Beijing, China
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