Evidence map›Paper›PMID 41009297›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

The Double-Edged Sword Effect of Interaction Frequency with AI on College Students: The Moderating Role of Peer Support.

Wenyan Sun, Zhanfeng Jiang, Shenyang Hai

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Wenyan SunSchool of MarXism, Henan Normal University, Xinxiang 453000, China.ORCID 0009-0009-0852-1701
Zhanfeng JiangSchool of MarXism, Henan Normal University, Xinxiang 453000, China.
Shenyang HaiInternational Business School, Hainan University, Haikou 570100, China.ORCID 0000-0002-9816-792X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (AI) has become an indispensable resource in contemporary higher education, providing substantial benefits to both students and institutions. As its adoption accelerates, it is important to balance these advantages against potential risks that may arise from students' varying levels of interaction with AI. Whereas most prior studies have focused on the favorable outcomes of AI for college students, the present research investigates its "double-edged sword" effects. Guided by social affiliation theory, a new model has been developed and empirically tested to clarify how and under what conditions the frequency of student-AI interaction influences social needs and behaviors. Longitudinal data obtained from 388 undergraduates showed that AI interaction frequency can shape prosocial behavior and problematic mobile phone use through a dual pathway involving the need for affiliation and feelings of loneliness. Furthermore, peer support moderates the indirect effect of AI interaction frequency on college students' prosocial behavior via their need for affiliation. The results showed that peer support did not significantly moderate the indirect relationship between AI interaction frequency and problematic mobile phone use through loneliness. Overall, our study extends the framework of the social affiliation theory and provides practical insights that guide the appropriate use of AI by college students, thereby supporting the development of healthy social skills and technology engagement in the age of AI.

Indexed as

artificial intelligencelonelinessneed for affiliationpeer supportproblematic mobile phone useprosocial behavior

Identifiers

PMID41009297
PMCPMC12466879

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.