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Cited by 7Digital Mental Health InterventionsArtificial Intelligence in Healthcare and EducationAI in Service Interactions

Practitioner Perspectives on the Uses of Generative AI Chatbots in Mental Health Care: Mixed Methods Study

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Goldie, Jessie, Dennis, Simon, Hipgrave, Lyndsey, Coleman, Amanda·2025·JMIR Human Factors
DOI 10.2196/71065PMC PMC12440320
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Abstract

Background: Generative artificial intelligence (AI) chatbots have the potential to improve mental health care for practitioners and clients. Evidence demonstrates that AI chatbots can assist with tasks such as documentation, research, counseling, and therapeutic exercises. However, research examining practitioners’ perspectives is limited. Objective: This mixed-methods study investigates: (1) practitioners’ perspectives on different uses of generative AI chatbots; (2) their likelihood of recommending chatbots to clients; and (3) whether recommendation likelihood increases after viewing a demonstration. Methods: Participants were 23 mental health practitioners, including 17 females and 6 males, with a mean age of 39.39 (SD 16.20) years. In 45-minute interviews, participants selected their 3 most helpful uses of chatbots from 11 options and rated their likelihood of recommending chatbots to clients on a Likert scale before and after an 11-minute chatbot demonstration. Results: Binomial tests found that Generating case notes was selected at greater-than-chance levels ( 15/23, 65%; P =.001), while Support with session planning ( P =.86) and Identifying and suggesting literature ( P =.10) were not. Although 55% (12/23) were likely to recommend chatbots to clients, a binomial test found no significant difference from the 50% threshold ( P =.74). A paired samples t test found that recommendation likelihood increased significantly (19/23, 83%; P =.002) from predemonstration to postdemonstration. Conclusions: Findings suggest practitioners favor administrative uses of generative AI and are more likely to recommend chatbots to clients after exposure. This study highlights a need for practitioner education and guidelines to support safe and effective AI integration in mental health care.

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  • The mental health support potential of LLM-CBs has been noted by both patients and practitioners.

    “The mental health support potential of LLM-CBs has been noted by both patients149,152,153 and practitioners153–155”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [155]

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Cited by

Sorted by citations

Authors
Goldie, J., Dennis, S., Hipgrave, L., & Coleman, A.
Affiliations
The University of Melbourne (AU)
Corresponding author
Dennis, Simon
ORCID
Venue
JMIR Human Factors
Publisher
JMIR Publications Inc.
Published
2025-09-16
Volume
12
Pages
e71065-e71065
ISSN
2292-9495 (ISSN-L)
DOI
10.2196/71065
PMC
PMC12440320
OpenAlex
W4413232168
Cited by
7
References
15
Citations by year
2026: 7
FWCI
5.60
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Human Factors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
Keywords
generative AI chatbots, mental health services, practitioner perspectives, clinical implementation of AI, session planning, practitioner education
MeSH terms
Generative Artificial Intelligence, Adult, Artificial Intelligence, Attitude of Health Personnel, Female, Health Personnel, Humans, Male, Mental Health Services, Middle Aged

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