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Cited by 198Impact of Technology on AdolescentsGender and Technology in EducationTechnostress in Professional Settings

AI Technology panic—is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents

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Huang, Shunsen, Lai, Xiaoxiong, Ke, Li, Li, Yajun, Wang, Huanlei, Zhao, Xinmei, Dai, Xinran, Wang, Yun·2024·Psychology Research and Behavior Management
DOI 10.2147/prbm.s440889PMC PMC10944174
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Abstract

Background: The emergence of new technologies, such as artificial intelligence (AI), may manifest as technology panic in some people, including adolescents who may be particularly vulnerable to new technologies (the use of AI can lead to AI dependence, which can threaten mental health). While the relationship between AI dependence and mental health is a growing topic, the few existing studies are mainly cross-sectional and use qualitative approaches, failing to find a longitudinal relationship between them. Based on the framework of technology dependence, this study aimed to determine the prevalence of experiencing AI dependence, to examine the cross-lagged effects between mental health problems (anxiety/depression) and AI dependence and to explore the mediating role of AI use motivations. Methods: = 13.21 ± 2.55) was used with a cross-lagged panel model and a half-longitudinal mediation model. Results: 17.14% of the adolescents experienced AI dependence at T1, and 24.19% experienced dependence at T2. Only mental health problems positively predicted subsequent AI dependence, not vice versa. For AI use motivation, escape motivation and social motivation mediated the relationship between mental health problems and AI dependence whereas entertainment motivation and instrumental motivation did not. Discussion: Excessive panic about AI dependence is currently unnecessary, and AI has promising applications in alleviating emotional problems in adolescents. Innovation in AI is rapid, and more research is needed to confirm and evaluate the impact of AI use on adolescents' mental health and the implications and future directions are discussed.

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  • In a longitudinal analysis of 3,843 adolescents across two time points, mental health problems (anxiety and depression) predicted LLM-CB dependence but not vice versa, mediated by escape motivation.

    “Huang et al. conducted a longitudinal analysis of 3,843 adolescents across two time points, finding that mental health problems (anxiety and depression) predicted LLM-CB dependence but not vice versa, mediated by escape motivation, again indicating that LLM-CB use may be a maladaptive coping strategy100.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [100]

    Methods (Participants) reports 3,843 adolescents in the final two-wave analysis. Results (Cross-Lagged Model) finds that anxiety and depression predicted later AI dependence, but not vice versa; Results (Half-Longitudinal Mediation) reports significant mediation by escape motivation. The study assessed dependence on AI technology broadly, however, not specifically on large-language-model chatbots (LLM-CBs), as Methods (Procedure) and the Discussion make clear. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • LLM-CB use may be a maladaptive coping strategy.

    “Huang et al. conducted a longitudinal analysis of 3,843 adolescents across two time points, finding that mental health problems (anxiety and depression) predicted LLM-CB dependence but not vice versa, mediated by escape motivation, again indicating that LLM-CB use may be a maladaptive coping strategy100.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [100]

    The Methods (Participants) report a two-wave analysis of 3,843 adolescents, and the Discussion reports that anxiety and depression predicted later AI dependence, not vice versa, with escape motivation as a mediator. The Methods (Procedure) define AI broadly—including assistants, chatbots and robots—so the findings support a possible maladaptive coping interpretation for AI use generally, but not specifically for LLM-chatbot use. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • A longitudinal correlational study showed that individual disposition (impaired mental health) predicted later problematic LLM-CB use but not vice versa.

    “A longitudinal correlational study also showed that individual disposition (impaired mental health) predicted later problematic LLM-CB use but not vice versa100”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [100]

    The Results (Results from the Cross-Lagged Model; page not available) report that depression and anxiety predicted later AI dependence among adolescents, while AI dependence did not predict later depression or anxiety. But Methods (Measurement; page not available) measures dependence on AI generally, and the Discussion (Relationship Between Mental Health Problems and AI Dependence; page not available) explicitly describes an overall evaluation of AI technology rather than a specific chatbot. The finding therefore does not establish the claimed direction specifically for problematic LLM-chatbot use. (LLM, conf=0.91)

    Experimental — accuracy not yet measured

  • Measures of AI dependence consist either of adaptations from other scales of behavioral addiction to technologies (e.g., social media, smartphone, or internet addiction) or self-developed measures.

    “Measures of “AI dependence” consist either of adaptations from other scales of behavioral addiction to technologies (e.g., social media, smartphone, or internet addiction)100–102, or self-developed measures103–106.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [100]

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Research works printed but not linked: 6

  1. Hamilton, 2023, Forbes Advis
  2. Hazan, 1994, Psychol Inq, 5, 1, 10.1207/s15327965pli0501 · DOI
  3. 1991, Escaping the Self: Alcoholism, Spirituality, Masochism, and Other Flights from the Burden of Selfhood
  4. Karmakar, 2020, AMCIS 2020 Proceedings, 19, 1
  5. 2017, Internet Addiction: Neuroscientific Approaches and Therapeutical Implications Including Smartphone Addiction
  6. Cho, 2019, J Korea Contents Assoc, 19, 138

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Authors
Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X., & Wang, Y.
Affiliations
Beijing Normal University (CN)
Corresponding author
Wang, Yun
ORCID
Venue
Psychology Research and Behavior Management
Publisher
Informa UK Limited
Published
2024-03
Volume
Volume 17
Pages
1087-1102
ISSN
1179-1578 (ISSN-L)
DOI
10.2147/prbm.s440889
PMC
PMC10944174
OpenAlex
W4392715481
Cited by
196
References
83
Citations by year
2026: 123 · 2025: 61 · 2024: 12
FWCI
139.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Psychology Research and Behavior Management (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Office for Philosophy and Social Sciences (ZD153)
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Gender Studies › Gender and Technology in Education
  • Social Sciences › Psychology › Social Psychology › Technostress in Professional Settings
Keywords
AI dependency, technology dependence, adolescents, mental health, anxiety, depression, escape motivation, social motivation, entertainment motivation, instrumental motivation, mediation

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