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Trust, Anxious Attachment, and Conversational AI Adoption Intentions in Digital Counseling: A Preliminary
Xiaoli Wu1, Kongmeng Liew1, Martin J Dorahy1
1School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand.
JMIR AI
|April 22, 2025
Summary
High trust in AI and anxious attachment styles predict greater adoption of conversational AI for mental health support. Understanding these factors is key for effective AI counseling services.
Area of Science:
- Human-Computer Interaction
- Psychology
- Artificial Intelligence
Background:
- Conversational artificial intelligence (CAI) offers accessible, affordable mental health support.
- CAI can deliver psychotherapy, psychoeducation, and emotional aid.
- User willingness to adopt CAI for mental health varies.
Purpose of the Study:
- Examine the association between dispositional trust in AI, attachment styles, and CAI adoption intentions.
- Investigate how individual psychological factors influence the uptake of AI mental health tools.
Main Methods:
- Cross-sectional survey of 239 American adults.
- Assessed attachment styles and dispositional trust in AI.
- Measured intentions to adopt CAI for mental health support after a vignette.
Main Results:
- Dispositional trust in AI significantly predicted CAI adoption intentions (P<.001).
- Attachment anxiety positively correlated with CAI adoption intentions (P=.04).
- Attachment avoidance did not significantly predict adoption intentions (P=.09).
Conclusions:
- Higher dispositional trust and attachment anxiety may increase the likelihood of adopting CAI for mental health.
- Further research on user psychology is crucial for safe and effective CAI counseling.
- Tailoring interventions based on trust and attachment can enhance CAI adoption.
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