Evidence map›Paper›PMID 40572032›Full record

ArticleAnnals of the New York Academy of Sciences2025

Artificial intelligence chatbots as a source of virtual social support: Implications for loneliness and anxiety management.

Kelly Merrill, Sai Datta Mikkilineni, Marco Dehnert

Abstract read
In one paragraph

Article in Annals of the New York Academy of Sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

3 authors.

Kelly MerrillSchool of Communication, Film, and Media Studies, University of Cincinnati, Cincinnati, Ohio, USA.ORCID 0000-0002-1221-3789
Sai Datta MikkilineniCollege of Communication & Information Sciences, The University of Alabama, Tuscaloosa, Alabama, USA.ORCID 0000-0003-4095-2180
Marco DehnertDepartment of Communication, University of Arkansas, Fayetteville, Arkansas, USA.ORCID 0000-0002-7456-0743

Funding

Charles Phelps Taft Research Center
6 · The paper itself

Abstract

Loneliness, social isolation, and anxiety affect millions of people across the world. Communication technologies, including artificial intelligence (AI) chatbots, can potentially offer support to those experiencing mental health challenges by providing companionship and support. Specifically, social AI can mimic human interaction, which may help alleviate loneliness and anxiety through person-centered messaging. Despite growing AI usage, there is limited research on the effectiveness of specific message types in this context. Thus, this study employed a 2 (person-centered message: high vs. low) × 2 (context: loneliness vs. anxiety) between-subjects design to test how different supportive messages from social AI chatbots impact subsequent outcomes. Results revealed that high person-centered messages are associated with increased emotional validation. Furthermore, the quality of social support and interpersonal warmth (IW) mediated the relationship between high person-centered messages and emotional validation. Finally, the mediation effect between high person-centered messages and emotional validation via the quality of emotional support was moderated by social presence, but not the mediation effect between high person-centered messages and emotional validation via IW. These results demonstrate the importance of developing social AI chatbots that employ messages high in person-centeredness, as these messages are most important for addressing mental health concerns.

Indexed as

AnxietyArtificial IntelligenceLonelinessSocial SupportAdultFemaleGenerative Artificial IntelligenceHumansMaleSocial InteractionYoung Adultanxietyartificial intelligence (AI)emotional validationinterpersonal warmth (IW)lonelinessperson‐centered messagessocial support

Identifiers

PMID40572032
PMCPMC12309430

What OpenQuestion holds

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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.