JMIR Publications

ABSTRACT

Background:

Despite the significant benefits of Artificial Intelligence (AI) in mental health, real-world implementation remains limited, making it essential to understand the factors that influence adoption.

Objective:

This study examined the acceptability and intention to use AI-based precision mental health technologies and proposed an empirical, theory-guided model that integrates traditional technology acceptance predictors (e.g., perceived usefulness, risk, ease of use…) with emerging psychological factors (e.g., AI anxiety, personality, conspiratorial thinking…) that are critical for effective implementation and strategic planning.

Methods:

An online survey was distributed to a sample of 357 psychologists in training, including both undergraduate and master’s students. A mixed-methods approach was used, combining quantitative measures (via psychometrically validated questionnaires) and qualitative data (through open-ended questions). Descriptive statistics and t-tests were conducted to characterize the sample, and responses to the open-ended questions on facilitators and barriers were thematically analyzed. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to build the empirical model.

Results:

Participants showed moderate-to-high acceptance and intention to use AI-based technologies, yet anxiety and perceived risk varied (higher in women), and more frequent use was linked to more favorable acceptance profiles without reducing fear. Thematic analysis revealed that participants viewed AI tools as efficiency-enhancing but raised concerns about reliability, usability, overdependence, and access constraints. PLS-SEM supported a hierarchical adoption pathway in which dispositional and demographic factors shape AI-related fear and perceived risk, which then influence cognitive evaluations and attitudes, ultimately predicting acceptance and intention to use AI-based precision mental health technologies. Predisposing variables (especially resistance to change and conspiratorial thinking) were the strongest predictors of AI-related anxiety, with gender and extraversion showing smaller but meaningful effects. Fear acted as a key affective mediator, increasing perceived risk and indirectly weakening positive attitudes and perceived usefulness. Acceptance was the most influential downstream construct, directly predicting satisfaction, perceived usefulness, prior experience, and future intention to use, consistent with a reinforcing feedback loop in which early acceptance supports sustained engagement.

Conclusions:

Findings support a layered implementation approach in line with contemporary frameworks, addressing (1) predisposing dispositional/emotional profiles, (2) precipitating fear and perceived risk via transparent regulation, explainable design, and policies that strengthen professional agency, and (3) maintenance through high-quality early experiences, usability, and sustained institutional support. This theory-guided model clarifies how psychological, contextual, and experiential factors jointly shape adoption and sustained use of AI-based precision mental health technologies, informing targeted training and implementation strategies.