Hope and Acceptance of AI-Guided Psychotherapy: Patient Adoption of Conversational AI in Mental Health Care
Author(s)
Felippe Ragghianti Ney Ferreira, Master1, Jorge Brantes Ferreira, PhD2, Priscila Correa Franco Amaral, PhD1, Fernanda Leao Ramos, PhD1.
1Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
1Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
OBJECTIVES: AI-powered conversational agents represent an innovative approach to expanding access to psychotherapy, particularly in settings where mental health services remain insufficient. Despite rapid technological advances, user acceptance remains a major barrier to large-scale impact. This study investigates the behavioral drivers of adoption of AI-guided psychotherapy, integrating technology acceptance constructs with hope (a motivational-affective variable rarely studied in this context). The study aims to contribute original insights into patient-centered diffusion of AI-based mental health technologies.
METHODS: A survey was administered to 220 Brazilian adult users (mean age 40, 68.2% male) who had direct experience interacting with a conversational AI designed for psychological support. The proposed model integrates constructs from the Technology Acceptance Model, including Usefulness, Ease of Use, Attitude, and Intention, alongside Self-Efficacy, Trust, and Hope. Structural equation modeling was used to assess relationships and model fit.
RESULTS: The proposed structural model explained 56.1% of the variance in Behavioral Intention to adopt AI-guided psychotherapy. Hope exerted a very strong and significant effect on Usefulness (0.928), and a moderate effect on Ease of Use (0.227). Usefulness was the principal determinant of Attitude (0.883) and also directly influenced Intention (0.608). Trust showed a positive but weak effect on Usefulness (0.184), and Ease of Use did not directly predict Attitude, an unexpected finding.
CONCLUSIONS: This study identifies Hope as a highly influential and previously underexplored determinant in the adoption of AI-based psychotherapy. Its strong effects highlight the role of motivational-affective states in technology appraisal for mental health interventions. Perceived ease of use was not a direct driver of behavioral intention, indicating that users may prioritize perceived therapeutic value and emotional resonance over ease of interaction. These insights expand the theoretical understanding of digital mental health adoption and suggest that interventions designed to cultivate hope in patients could significantly enhance uptake and sustained engagement with AI-guided psychotherapy.
METHODS: A survey was administered to 220 Brazilian adult users (mean age 40, 68.2% male) who had direct experience interacting with a conversational AI designed for psychological support. The proposed model integrates constructs from the Technology Acceptance Model, including Usefulness, Ease of Use, Attitude, and Intention, alongside Self-Efficacy, Trust, and Hope. Structural equation modeling was used to assess relationships and model fit.
RESULTS: The proposed structural model explained 56.1% of the variance in Behavioral Intention to adopt AI-guided psychotherapy. Hope exerted a very strong and significant effect on Usefulness (0.928), and a moderate effect on Ease of Use (0.227). Usefulness was the principal determinant of Attitude (0.883) and also directly influenced Intention (0.608). Trust showed a positive but weak effect on Usefulness (0.184), and Ease of Use did not directly predict Attitude, an unexpected finding.
CONCLUSIONS: This study identifies Hope as a highly influential and previously underexplored determinant in the adoption of AI-based psychotherapy. Its strong effects highlight the role of motivational-affective states in technology appraisal for mental health interventions. Perceived ease of use was not a direct driver of behavioral intention, indicating that users may prioritize perceived therapeutic value and emotional resonance over ease of interaction. These insights expand the theoretical understanding of digital mental health adoption and suggest that interventions designed to cultivate hope in patients could significantly enhance uptake and sustained engagement with AI-guided psychotherapy.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MT25
Topic
Medical Technologies
Topic Subcategory
Digital Health
Disease
Mental Health (including addition)