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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 6, 2025
Date Accepted: Jul 23, 2026

The final, peer-reviewed published version of this preprint can be found here:

Integrating AI-Powered Chatbots Into Patient Education From the Perspectives of Patients, Caregivers, and Nurses: Qualitative Study

Liu Z, Li Y, Wang J, Liu Q, Chen F, Zhu L, Ren Y, Xing L, Wang X

Integrating AI-Powered Chatbots Into Patient Education From the Perspectives of Patients, Caregivers, and Nurses: Qualitative Study

J Med Internet Res 2026;28:e87254

DOI: 10.2196/87254

PMID: 42531560

Integrating AI-Powered Chatbots Into Patient Education From the Perspectives of Patients, Caregivers, and Nurses: A Qualitative Study

  • Zihao Liu; 
  • Yuli Li; 
  • Jingjing Wang; 
  • Qing Liu; 
  • Feife Chen; 
  • Lifeng Zhu; 
  • Yanbei Ren; 
  • Linlin Xing; 
  • Xiaoyun Wang

ABSTRACT

Background:

AI-powered chatbots offer new opportunities to enhance patient education; however, their integration may reshape patterns of information interactions and trust relationships among patients, caregivers, and nurses. Evidence remains limited regarding how these stakeholders perceive the value and risks of AI-powered chatbots and their potential influence on nurse-patient trust.

Objective:

This study aimed to explore patients’, caregivers’, and nurses’ attitudes toward and experiences with integrating AI-powered chatbots into patient education and to identify perceived benefits, implementation challenges, potential effects on trust, and the supportive conditions required for safe integration.

Methods:

This qualitative study was conducted from April to July 2025. Patients and caregivers were recruited from a tertiary general hospital using maximum variation purposive sampling, while nurses were recruited through snowball sampling from six hospitals of varying tiers. Data were collected using a sociodemographic questionnaire and semi-structured, in-depth interviews. Interview recordings were transcribed verbatim and analyzed using reflexive thematic analysis, with NVivo used for coding and theme construction. Sociodemographic data were analyzed descriptively.

Results:

A total of 60 participants were included: 29 patients, 17 caregivers, and 14 nurses. Four themes were identified: perceptions and maintenance of nurse-patient trust, conditional acceptance and practical needs, functional optimization and implementation safeguards, and nurse role pressures and competency restructuring. All three stakeholder groups recognized the potential of AI-powered chatbots to address unmet information support needs in patient education but expressed reservations about their accuracy, personalization, and transparency. AI-powered chatbots were not perceived as a direct threat to nurse-patient trust. However, nurses were more sensitive to potential trust tensions, increased explanation burden, and expanded professional responsibilities, highlighting the need for competency restructuring. Stakeholder groups also differed in their perceptions of the conditions required to maintain nurse-patient trust. Limited digital health literacy and the digital divide affecting older patients were major barriers to integrating AI-powered chatbots into patient education.

Conclusions:

Patients, caregivers, and nurses were generally cautiously open to integrating AI-powered chatbots into patient education, although their assessments of benefits and risks differed by role. AI-powered chatbots may be best positioned as adjunctive information support tools. Their safe use should be tailored to patient characteristics, information risk, and clinical context, with nurses’ professional oversight and coordinated support across governance, technical, and clinical implementation levels.


 Citation

Please cite as:

Liu Z, Li Y, Wang J, Liu Q, Chen F, Zhu L, Ren Y, Xing L, Wang X

Integrating AI-Powered Chatbots Into Patient Education From the Perspectives of Patients, Caregivers, and Nurses: Qualitative Study

J Med Internet Res 2026;28:e87254

DOI: 10.2196/87254

PMID: 42531560

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