Evidence map›Paper›PMID 42552393›Full record

ArticleNature human behaviour2026

Interaction with AI companions and psychological well-being.

Yutong Zhang, Dora Zhao, Jeffrey T Hancock, Robert Kraut, Diyi Yang

Abstract read
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In one paragraph

Article in Nature human behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Yutong ZhangDepartment of Computer Science, Stanford University, Stanford, CA, USA. yutongz7@stanford.edu.ORCID http://orcid.org/0009-0007-6027-3122
Dora ZhaoDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Jeffrey T HancockDepartment of Communication, Stanford University, Stanford, CA, USA.
Robert KrautSchool of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0002-8554-2137
Diyi YangDepartment of Computer Science, Stanford University, Stanford, CA, USA. diyiy@cs.stanford.edu.ORCID http://orcid.org/0000-0003-1220-3983

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As large-language-model-enhanced chatbots become increasingly expressive and socially responsive, many users begin forming companionship-like bonds with them. This study investigates how using AI companions relates to psychological well-being. We collected self-reported data from 1,131 US adults who use Character.AI, including survey responses and 4,664 chat sessions (464,687 messages) from 237 participants. By triangulating self-reported usage, relationship descriptions and real chat histories, we identify patterns of engagement and associated outcomes. Smaller social networks were associated with reporting companionship as the primary chatbot use (β = -0.03; 95% confidence interval (CI), (-0.05, -0.01)), which in turn was associated with lower well-being (β = -0.48; 95% CI, (-0.70, -0.25)). For self-reported companionship usage, this association was stronger when interactions were intensive (β = -0.31; 95% CI, (-0.56, -0.06)) and highly disclosive (β = -0.38; 95% CI, (-0.63, -0.14)). These results suggest that the association between AI companionship and well-being is not uniform and depends on users' offline social environments and how chatbots are used.

Identifiers

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.