Evidence map›Paper›PMID 38495087›Full record

ArticlePsychology research and behavior management2024

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.

Shunsen Huang, Xiaoxiong Lai, Li Ke, Yajun Li, Huanlei Wang, Xinmei Zhao, Xinran Dai, Yun Wang

Abstract read
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Article in Psychology research and behavior management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shunsen HuangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.ORCID 0000-0002-9331-3107
Xiaoxiong LaiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.ORCID 0000-0002-5760-0918
Li KeState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.
Yajun LiShenzhen Institute of Education Sciences, Shenzhen, 518001, People's Republic of China.
Huanlei WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.
Xinmei ZhaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.
Xinran DaiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.
Yun WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, People's Republic of China.ORCID 0000-0002-2648-4467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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: A two-wave cohort program with 3843 adolescents (Male = 1848, 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.

Indexed as

adolescentsanxietyartificial intelligence dependenceartificial intelligence use motivationdepressionmental health

Identifiers

PMID38495087
PMCPMC10944174

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.