Attitudes Toward the Adoption of two AI-Enabled Mental Health Tools Among Prospective Psychotherapists: A Cross-Sectional Study

Abstract

Background: An investigation of individual-level predictors of the intention to use an AI-enabled feedback and a treatment recommendation tool in mental healthcare. Objective: Using an extended UTAUT model to gain insight into the predictors of technology usage intentions for two specific AI-enabled mental healthcare tools. Methods: A cross-sectional study included 206 psychology students and psychotherapists-in-training to examine the predictors of their intention to use two AI-enabled mental healthcare tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapist may use for treatment decisions. Participants were presented with graphic depictions of the tools’ functioning mechanisms before measuring the variables of an extended UTAUT. Two structural equation models (one for each tool) were specified that included direct and mediated paths predicting tool usage intentions. Results: Perceived usefulness and social influence had a positive effect on the intention to use both tools. However, trust was unrelated to usage intention for both tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to usage intentions when considering all predictors. In addition, a direct relationship between cognitive technology readiness and the intention to use the feedback tool, and a negative relationship between AI anxiety and usage intentions were observed. Conclusions: The results shed light on general and tool-dependent drivers of AI technology adoption in mental healthcare. Future research may explore technological and user group characteristics that influence the adoption of AI-enabled tools in mental healthcare