ArticleJMIR AI2025
Trust, Anxious Attachment, and Conversational AI Adoption Intentions in Digital Counseling: A Preliminary Cross-Sectional Questionnaire Study.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Mitigating the Negative Effects of Internet Browsing on Young People's Resilience and Outlook on Life Through Classic Grimms' Fairy Tales: Exploratory Randomized Controlled Study.JMIR formative research · 2025Trial
- Trusting and staying with AI doctors: cognitive and emotional pathways driving continuance intention toward AI healthcare chatbots.Frontiers in public health · 2026Article
- Mentalizing Without a Mind: Psychotherapeutic Potential of Generative AI.Journal of medical Internet research · 2025Article
- Techno-emotional projection in human-GenAI relationships: a psychological and ethical conceptual perspective.Frontiers in psychology · 2025Review
- Healthcare Professionals' perspectives on AI-driven decision support in young adult mental health: an analysis through the lens of a shared decision-making framework.Frontiers in digital health · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundConversational artificial intelligence (CAI) is increasingly used in various counseling settings to deliver psychotherapy, provide psychoeducational content, and offer support like companionship or emotional aid. Research has shown that CAI has the potential to effectively address mental health issues when its associated risks are handled with great caution. It can provide mental health support to a wider population than conventional face-to-face therapy, and at a faster response rate and more affordable cost. Despite CAI's many advantages in mental health support, potential users may differ in their willingness to adopt and engage with CAI to support their own mental health.
objectiveThis study focused specifically on dispositional trust in AI and attachment styles, and examined how they are associated with individuals' intentions to adopt CAI for mental health support.
methodsA cross-sectional survey of 239 American adults was conducted. Participants were first assessed on their attachment style, then presented with a vignette about CAI use, after which their dispositional trust and subsequent adoption intentions toward CAI counseling were surveyed. Participants had not previously used CAI for digital counseling for mental health support.
resultsDispositional trust in artificial intelligence emerged as a critical predictor of CAI adoption intentions (P<.001), while attachment anxiety (P=.04), rather than avoidance (P=.09), was found to be positively associated with the intention to adopt CAI counseling after controlling for age and gender.
conclusionsThese findings indicated higher dispositional trust might lead to stronger adoption intention, and higher attachment anxiety might also be associated with greater CAI counseling adoption. Further research into users' attachment styles and dispositional trust is needed to understand individual differences in CAI counseling adoption for enhancing the safety and effectiveness of CAI-driven counseling services and tailoring interventions.
trial registrationOpen Science Framework; https://osf.io/c2xqd.
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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.