Interaction with AI companions and psychological well-being | Nature Human Behaviour

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. This is a preview of subscription content, access via your institution Access options Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Learn more Subscribe to this journal Receive 12 digital issues and online access to articles 111,21 € per year only 9,27 € per issue Learn more Buy this article Purchase on SpringerLink Instant access to the full article PDF. 39,95 € Prices may be subject to local taxes which are calculated during checkout Fig. 1: Study overview of how human–chatbot companionship, offline social support and well-being are interrelated. Fig. 2: Characterizing chatbot usage types through user-reported data and chat content analysis. Fig. 3: Interaction between companionship use and other interaction measures in predicting well-being. Fig. 4: Topic modelling analysis of user-donated chat histories and self-reported reflections on chatbot interactions. Explore related subjects Discover the latest articles and news in related subjects. Human behaviour Science, technology and society Data availability The de-identified quantitative data are available via GitHub at https://github.com/SALT-NLP/AI-companionship-well-being . For the qualitative data, raw chat history data are not publicly available because they contain potentially sensitive personal information and were collected under consent agreements that do not permit data sharing. Even after de-identification, the conversational nature of the data poses a substantial risk of participant re-identification. To support interpretation and verification of the qualitative findings while preserving participant privacy, we provide de-identified and processed materials as well as the prompts we used in the Supplementary Information , including all the prompts (Supplementary Information sections 1.11 – 1.15 ), evaluation procedures for the topic modelling pipeline (Supplementary Information section 1.4.1 ), chatbot–user conversation topics (Supplementary Information section 1.5 ) and self-disclosure topics (Supplementary Information section 1.6 ), as well as positive and negative influence topics derived from topic modelling (Supplementary Information section 1.7 ), illustrated with paraphrased excerpts from participant self-report survey responses. We also include the topic model figure (Fig. 4 ) to show the categories derived from the data and their relative volumes. 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Ziems, J. Huang and W. Liang for their helpful feedback on this work. We also thank members of the Stanford SALT lab and the Stanford CS 224C course for their suggestions at different stages of this project. This work is supported in part by grants from the NSF CAREER IIS-2247357 (D.Y.), ONR N00014-24-1-2532 (D.Y.), Sloan Foundation (D.Y.) and NIMH R01MH139114-01 (D.Y.). D.Z. is supported in part by the Paul and Daisy Soros Fellowship for New Americans. R.K. is supported by NIMH 60663.1.1090827. Author information Authors and Affiliations Department of Computer Science, Stanford University, Stanford, CA, USA Yutong Zhang, Dora Zhao & Diyi Yang Department of Communication, Stanford University, Stanford, CA, USA Jeffrey T. Hancock School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Robert Kraut Authors Yutong Zhang View author publications Search author on: PubMed Google Scholar Dora Zhao View author publications Search author on: PubMed Google Scholar Jeffrey T. Hancock View author publications Search author on: PubMed Google Scholar Robert Kraut View author publications Search author on: PubMed Google Scholar Diyi Yang View author publications Search author on: PubMed Google Scholar Contributions Y.Z. and D.Z. designed the study, collected the data and conducted the data analysis with guidance from D.Y. R.K. and J.T.H. provided guidance on the interpretation of results. Y.Z. wrote the manuscript, and all authors contributed to manuscript revisions. Corresponding authors Correspondence to Yutong Zhang or Diyi Yang . Ethics declarations Competing interests The authors declare no competing interests. Peer review Peer review information Nature Human Behaviour thanks Minjin Rheu, Tianling Xie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Supplementary Information (download PDF ) Supplementary Tables 1–10, Discussion and Prompts. Reporting Summary (download PDF ) Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions About this article Cite this article Zhang, Y., Zhao, D., Hancock, J.T. et al. Interaction with AI companions and psychological well-being. Nat Hum Behav (2026). https://doi.org/10.1038/s41562-026-02516-2 Download citation Received : 27 June 2025 Accepted : 03 June 2026 Published : 04 August 2026 Version of record : 04 August 2026 DOI : https://doi.org/10.1038/s41562-026-02516-2 Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy shareable link to clipboard Provided by the Springer Nature SharedIt content-sharing initiative