Attitudes Toward Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists

Principal Investigator(s):  View help for Principal Investigator(s) Anne-Kathrin Kleine, LMU Munich

Version:  View help for Version V1

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Project Citation: 

Kleine, Anne-Kathrin. Attitudes Toward Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists . Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-12-14. https://doi.org/10.3886/E195822V1

Project Description

Summary:  View help for Summary
Background: Recent efforts to make artificial intelligence (AI) applications in clinical care more user-friendly have faced adoption barriers. There is a lack of research on the application of AI systems in mental health care specifically.

Objective: In an attempt to fill this research gap, this study focuses on factors influencing the likelihood of psychology students and early practitioners adopting two specific AI-enabled mental health tools. These tools have been evaluated with reference to the Unified Theory of Acceptance and Use of Technology.

Methods: A cross-sectional study with a sample size of 206 psychology students and trainee psychotherapists was undertaken. The participants' openness to using two AI tools was evaluated. The first tool provides feedback to therapists on their motivational interviewing techniques and the second one uses patient voice samples to provide mood scores for better treatment decisions. Participants were presented with visual explanations of each tool's functions before their responses were measured based on the Unified Theory of Acceptance and Use of Technology. Two structural equation models were created to predict tool use intentions.

Results: Both perceived usefulness and social influence had a positive impact on participants' willingness to use both tools. However, users' trust in the technology did not appear to influence their choice to use it. Interestingly, perceived ease of use was found to be unrelated or even negatively related to use intentions. Additionally, a positive correlation was found between readiness to adopt technology and the intention to use the feedback tool while AI anxiety had a negative correlation with the use intention for both tools.

Conclusions: The data sheds light on both generic and tool-specific drivers of AI technology adoption in the mental health care field. Further study is encouraged to better understand the technological and user group dynamics impacting the adoption of AI in this area.
Funding Sources:  View help for Funding Sources Volkswagen Stiftung (Germany) (98 525)

Scope of Project

Subject Terms:  View help for Subject Terms artificial intelligence; mental health services ; students
Geographic Coverage:  View help for Geographic Coverage Canada, United States, United Kingdom, Germany
Time Period(s):  View help for Time Period(s) 11/2/2022 – 3/12/2023
Collection Date(s):  View help for Collection Date(s) 11/2/2022 – 3/12/2023
Universe:  View help for Universe Psychology students and psychotherapists in training in Germany and English-speaking countries worldwide.
Data Type(s):  View help for Data Type(s) survey data

Methodology

Response Rate:  View help for Response Rate A total of 362 participants began the questionnaire. Of these, 208 provided answers on the behavioral intention to use the tools, resulting in a 57.5% response rate. In addition, 2 participants failed at least 2 of the 4 attention check items, leaving us with a final sample size of 206.
Sampling:  View help for Sampling Convenience sampling was applied. Participants were selected based on their availability and willingness to participate. They were recruited through social media postings, email correspondence, and professional research-focused panel companies.
Collection Mode(s):  View help for Collection Mode(s) web-based survey
Scales:  View help for Scales A Likert-type scale was used.
Unit(s) of Observation:  View help for Unit(s) of Observation Individuals

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V1 [2023-12-14]

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