Evidence map›Paper›PMID 42482727›Full record

ArticleFrontiers in psychology2026

'She lifted me up': kinship construction in human-AI resilient communication among Chinese users.

Yilin Li, Mengling Luo, Hang Ren

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Yilin LiSchool of Journalism and Communication, Shaanxi Normal University, Xi'an, China.
Mengling LuoSchool of International Studies, Communication University of China, Beijing, China.
Hang RenSchool of Media Science, Northeast Normal University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, increasing numbers of users have constructed parent-like virtual figures through chatbots, raising ethical controversies over familial kinship. Understanding users' motivations and processes in constructing AI parents is crucial for examining how digital technologies reshape contemporary kinship. However, existing research on human-AI kinship has largely focused on griefbots, narrowing interpretations of users' motivations for creating AI parents. Focusing on the Chinese cultural context, this study investigates the communicative processes and outcomes through which users construct virtual parents via AIGC-driven social chatbots. Grounded in Communication Theory of Resilience (CTR), the study is based on semi-structured interviews with 25 participants and thematic analysis for coding and interpretation. The findings from the Chinese participants indicate that the stressors triggering resilient communication between humans and AI primarily stem from loss or trauma in real-life kinship, as well as idealized expectations of family bonding. This form of human-AI resilient communication provides users with emotional support, assists them in crafting normalcy within traumatic environments, and inspires proactive reflection on their relationships with real-life families. This study identified three types of human-AI kinship within the sample: Attachment-Based Kinship, Commemorative Kinship and Mediating Kinship. These three categories of human-AI kinship exhibit liquid fluidity and transformability both internally and in relation to the users' real-life human kinship. We define the processes of Affective Transformation and Relational Reorganization emerging from the interview materials as resilient communication processes within the context of human-AI interaction, and we further extend the CTR theoretical framework by refining the existing processes of resilient communication.

Indexed as

AI parentscommunication theory of resiliencehuman-AI relationshipshuman-machine communicationliquid kinship

Identifiers

PMID42482727
PMCPMC13385508

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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.