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Cited by 56Technology Adoption and User BehaviourDigital Mental Health InterventionsArtificial Intelligence in Healthcare and Education

Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study

Open access
Kleine, Anne-Kathrin, Kokje, Eesha, Lermer, Eva, Gaube, Susanne·2023·JMIR Human Factors
DOI 10.2196/46859PMC PMC10372564

Published version of a preprint

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Abstract

Background: Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. Objective: This study aimed to address this gap by examining the predictors of psychology students’ and early practitioners’ intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. Methods: This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care 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 therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools’ functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. Results: Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. Conclusions: The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.

AI snapshot

Written from the paper’s full text.

TL;DR

In a cross-sectional survey of 206 psychology students and psychotherapists in training, perceived usefulness and social influence positively predicted intention to use two AI-enabled mental health tools, while trust did not. Perceived ease of use was unrelated for the feedback tool but negatively related for the treatment recommendation tool, and AI anxiety negatively predicted intentions for both.

Key findings

  • Perceived usefulness and social influence positively predicted intention to use both tools: feedback tool (PU β=.51, P<.001; SI β=.32, P<.001) and treatment recommendation tool (PU β=.28, P=.01; SI β=.50, P<.001).
  • Trust was unrelated to use intentions for both tools (feedback tool β=.04, P=.72; treatment recommendation tool β=.17, P=.12).
  • Perceived ease of use was unrelated for the feedback tool (β=.03, P=.59) and negatively related for the treatment recommendation tool (β=−.18, P=.004).
  • Cognitive technology readiness was positively related to feedback tool intention (β=.12, P=.02), while AI anxiety was negatively related to both feedback tool (β=−.18, P=.001) and treatment recommendation tool (β=−.21, P<.001) intentions; a negative mediation path appeared for treatment recommendation tool via perceived ease of use (TU→PE→IU β=−.10, P=.01).

Methods

  • Cross-sectional web-based survey of 206 psychology students and psychotherapists in training, recruited via social media, university/training-center emails, and Prolific; data collected October 2022–January 2023 from Germany, the United States, the United Kingdom, and Canada.
  • Participants viewed graphic depictions of two AI-enabled tools—a psychotherapy feedback tool on motivational interviewing adherence and a treatment recommendation tool using voice-derived mood scores—then completed extended Unified Theory of Acceptance and Use of Technology measures plus tool understanding, cognitive technology readiness, AI anxiety, privacy concerns, and demographics.
  • Confirmatory factor analysis and two structural equation models (one per tool) were estimated in R using lavaan, including direct and mediated paths; hypotheses were preregistered.

Limitations

  • Stated: cross-sectional data prevent assessing order of effects; UTAUT/TAM models are criticized as oversimple and should be integrated with organizational/system processes; the sample focused on psychology students and psychotherapists in training, so future work should compare multiple user groups and tools.
  • Evident from the text: the outcome is self-reported intention to use, not observed adoption or actual use.
  • Evident from the text: the final sample was 80.1% women and 53.9% studied in Germany, and the study had a 42.54% dropout rate before attention checks, which may limit generalizability.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.2196/46859

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 32 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Perceived usefulness had a positive effect on the intention to use the feedback tool.

    “Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001)”
    Quote in the paperSubstantial claim
  • Findingown data

    The theoretical model fitted the data adequately for the feedback tool.

    “The theoretical model fitted the data adequately (FB tool: χ2370=808.9, P<.001; CFI=0.89; TLI=0.87; RMSEA=0.08; SRMR=0.08”
    Quote in the paperSubstantial claim
  • Findingown data

    Perceived ease of use was unrelated to intention to use the feedback tool when considering all predictors.

    “Moreover, perceived ease of use was unrelated (feedback tool)”
    Quote in the paperNot a substantial claim
  • Findingown data

    The relationship of tool understanding with intention to use the feedback tool is not mediated through perceived usefulness, perceived ease of use, or trust.

    “the relationships of tool understanding and cognitive technology readiness with the intention to use FB tool are not mediated through perceived usefulness, perceived ease of use, or trust.”
    Quote in the paperSubstantial claim
  • Findingown data

    Cognitive technology readiness had a positive relationship with intention to use the feedback tool.

    “In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool”
    Quote in the paperSubstantial claim
  • May combine two claims. 8
  • The quote only partly states this claim. 1
  • Classified as probably not a substantial claim. 22

  • May combine two claims.

    Findingreasoning

    AI anxiety was negatively related to the intention to use both the feedback and treatment recommendation tools.

    “Trust in the tools was unrelated, whereas AI anxiety was negatively related to the intention to use both the FB and TR tools.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Intention to use an AI-enabled mental health tool predicts actual adoption or use of the tool.

  • The quote only partly states this claim.

    Findingown data

    Perceived ease of use was negatively related to the intention to use the treatment recommendation tool when considering all predictors in one model.

    “However, trust was unrelated to the intention to use both tools, and perceived ease of use was unrelated (FB tool) and even negatively related (TR tool) to the intention to use when considering all predictors in 1 model.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Intention to use an AI-enabled mental health tool predicts actual adoption or use of the tool.

  • Classified as probably not a substantial claim.

    Premisecitation

    The UTAUT considers perceived usefulness, perceived ease of use, social processes, and demographic variables as predictors of use intention.

    “In addition to perceived usefulness (ie, performance expectancy) and perceived ease of use (ie, effort expectancy), it considers social processes (ie, social influence) and demographic variables (ie, age and gender) as predictors of use intention [11].”
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    Premisecitation

    Several studies have demonstrated the applicability of the UTAUT in investigating the implementation of AI-CDSSs.

    “Several studies have already demonstrated the applicability of the UTAUT in investigating the implementation of AI-CDSSs [12-17].”
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    • cites [14] Lin, H-C, 2021, Educ Technol Soc

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    Study design and conductcitation

    The hypotheses were preregistered in the Open Science Framework.

    “The hypotheses were preregistered in the Open Science Framework [33].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Student mental health AI toolsOpen Source Fremwork2023-06-26https://osf.io/fqdzb

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Timely psychotherapeutic support may lower the risk of worsening depressive symptoms and suicidality.

    “Timely psychotherapeutic support may lower the risk of worsening depressive symptoms and suicidality [25].”
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    Premisecitation

    Some evidence exists for the association of AI acceptance with age and country.

    “some evidence exists for the association of AI acceptance with age [45] and country [46].”
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    Premisecitation

    The UTAUT combines the principles of 8 technology acceptance models, including the TAM.

    “The UTAUT combines the principles of 8 technology acceptance models, including the TAM.”
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    Premisecitation

    Male participants have more positive attitudes toward AI technologies than female participants.

    “it has been shown that male participants have more positive attitudes toward AI technologies than female participants [44].”
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    Study design and conductown data

    No inflated standard errors were observed, and the SEM was interpreted without regularization.

    “No inflated SEs were observed, and we proceeded with the interpretation of the SEM without regularization.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Multiple studies have demonstrated the effectiveness of AI-enabled emotion analysis in assessing patients' depressive states and recommending timely intervention, thereby improving mental health care.

    “Multiple studies have demonstrated the effectiveness of AI-enabled emotion analysis in assessing patients’ depressive states and recommending timely intervention, thereby improving mental health care [22,26].”
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    Premisecitation

    Data privacy concerns and AI anxiety have repeatedly been identified as negative predictors of the intention to use AI technology.

    “Data privacy concerns and AI anxiety (ie, fears and insecurity regarding AI technology) have repeatedly been identified as negative predictors of the intention to use AI technology [43].”
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    • cites [43] source

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    Interpretationcitation

    The consistent link between social influence and AI use intentions found in studies using student samples may be explained by the greater susceptibility of students to influence of peers and prospective employers.

    “This consistent link between social influence and AI use intentions found in studies using student samples may be explained by the greater susceptibility of students to influence of peers and prospective employers [53].”
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    • cites [53] source

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    Interpretationcitation

    Students have yet to develop a professional identity that shapes their work-related decisions, so they may be more likely to align their decisions with the perceived expectations of influential others.

    “As students have yet to develop a professional identity that shapes their work-related decisions, they may be more likely to align their decisions with the perceived expectations of influential others [54].”
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    • cites [54] source

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    Premisecitation

    The results of that study suggest a link between perceived social norms, perceived ease of use, perceived usefulness, and perceived knowledge with students' intention to use AI-enabled tools.

    “The results suggest a link between the perceived social norms, perceived ease of use, perceived usefulness, and perceived knowledge with students’ intention to use AI-enabled tools.”
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    • cites [17] source

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  • May combine two claims.

    Interpretationreasoning

    Ease of use may positively predict learning intentions but maybe not the intention to use high-stakes mental health tools among students and trainees who have not yet gained profound professional experience.

    “That is, ease of use may positively predict learning intentions but maybe not the intention to use high-stakes mental health tools among students and trainees who have not yet gained profound professional experience.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI anxiety represents intuitive, affective reactions, such as feeling apprehensive about the tool or being hesitant to use the tool for fear of making mistakes.

    “In contrast, AI anxiety represents intuitive, affective reactions, such as feeling apprehensive about the tool or being hesitant to use the tool for fear of making mistakes [32].”
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    • cites [32] source

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    Study design and conductreasoning

    Focusing on 2 specific AI-enabled mental health tools may be highlighted as a strength of this study, as it increases the ecological validity of the results.

    “In this vein, focusing on 2 specific AI-enabled mental health tools may be highlighted as a strength of this study, as it increases the ecological validity of the results.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    A study predicting intentions to learn about AI applications among medical staff found that perceived ease of use was the strongest predictor of the intention to learn how to use AI-enabled tools in health care.

    “This interpretation might be supported by a study predicting intentions to learn about AI applications among medical staff [55], which found that perceived ease of use was the strongest predictor of the intention to learn how to use AI-enabled tools in health care.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [55] Lin, H-C, 2021, Educ Technol Soci

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    One explanation for trust being unrelated may be participants' limited insight into the functioning mechanisms of the tools.

    “One explanation for this finding may be participants’ limited insight into the functioning mechanisms of the tools.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    General AI understanding may spur use intentions of low-stakes AI-generated advice but not the intention to use AI advice for deriving treatment decisions.

    “This might indicate that general AI understanding may spur use intentions of low-stakes AI-generated advice but not the intention to use AI advice for deriving treatment decisions.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    One explanation for the null and negative relationships between perceived ease of use and use intentions for AI-generated recommendations in mental health may be the high stakes of accepting the tool's advice.

    “One explanation for the null and negative relationships between perceived ease of use and use intentions for AI-generated recommendations in the mental health field may be the high stakes of accepting the tool’s advice.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    As students and psychotherapists in training have limited to no experience interacting with AI-generated feedback, they may base their decision-making on intuitive, emotional reactions better represented by AI anxiety than trust in the tools.

    “As students and psychotherapists in training have limited to no experience interacting with AI-generated feedback, they may base their decision-making on intuitive, emotional reactions better represented by AI anxiety than trust in the tools [56].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [56] source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Zhai et al reported positive relationships between perceived usefulness and social influence with the intention to use AI-assisted contouring technology among radiation oncologists.

    “Zhai et al [15] reported positive relationships between perceived usefulness and social influence with the intention to use AI-assisted contouring technology among radiation oncologists.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] source

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  • Classified as probably not a substantial claim.

    Interpretationcitation

    By differentiating between specific tool understanding and more general cognitive technology readiness, this study moves beyond previous research that focused on the role of general AI knowledge in predicting general use intention.

    “By differentiating between specific tool understanding and more general cognitive technology readiness, this study moves beyond previous research that focused on the role of general AI knowledge in predicting general use intention [17].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [17] source

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  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The assessment of symptom severity often involves complex interactions with the patient and reflections on psychotherapeutic elements, which may make participants skeptical of a device that is perceived as being easy to use.

    “The assessment of symptom severity often involves complex interactions with the patient and reflections on psychotherapeutic elements, which may make participants skeptical of a device that is perceived as being easy to use.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    A profound assessment of trust in the tools requires more in-depth knowledge than assessing AI anxiety.

    “A profound assessment of their trust in the tools requires more in-depth knowledge than assessing their AI anxiety.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Gado et al found support for the direct effects of perceived usefulness, AI knowledge, and perceived social norms on the intention to use AI as well as indirect effects of perceived ease of use on use intention via positive attitudes toward AI in a sample of psychology students.

    “Gado et al [17] found support for the direct effects of perceived usefulness, AI knowledge, and perceived social norms on the intention to use AI as well as indirect effects of perceived ease of use on use intention via positive attitudes toward AI in a sample of psychology students.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] source

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28 of 28

  • The paper relies on this without support.

    If system and corporate requirements for implementing a technology are satisfied, its successful deployment depends on the practitioner’s willingness to use it.

    “If the system and corporate requirements for implementing a given technology are satisfied, their successful deployment depends on the practitioner’s willingness to use them.”

  • The paper relies on this without support.

    Prospective and current mental health practitioners may have varying levels of skepticism about implementing AI technology for different purposes in their future practice.

    “However, prospective and current mental health practitioners may have varying levels of skepticism about implementing AI technology for different purposes in their (future) practice.”

  • The paper relies on this without support.

    There is a lack of specificity in assessing use intention, limiting the utility of these findings in informing practice.

    “Although research has begun to examine practitioners’ acceptance of AI-enabled tools in mental health care, there is a lack of specificity in assessing use intention, limiting the utility of these findings in informing practice.”

  • The paper relies on this without support.

    Intention to use an AI-enabled mental health tool predicts actual adoption or use of the tool.

    “To investigate the implementation challenges and potential user needs, in this study, we examined the intention to use 2 AI-enabled mental health care tools among psychology students and psychotherapists in training.”

  • The paper relies on this without support.

    Psychology students and psychotherapists in training are a potential user group for AI-enabled mental health care tools.

    “Finally, we focused on psychology students and psychotherapists in training as a potential user group”
5 of 5

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of AI are key contributors to adoption.

    “Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of the AI are key contributors to adoption [19,45,62-67].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [66]

    The Results section reports that perceived usefulness was positively related to intention to use both AI mental-health tools. It also reports that perceived ease of use was unrelated to intention to use one tool and negatively related for the other, so the paper does not support presenting ease of use as a general positive contributor. The Introduction discusses accuracy and reliability as sources of clinicians’ concern, but the study does not establish them, perceived benefit, or usability as adoption predictors. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Figure caption attributes reference 23 to the wrong authors

    Minor

    The Figure 1 caption calls reference 23 a study by Venkatesh et al, but reference 23 lists Rønnestad and Ladany as its authors.

  • Treatment-tool country mean is outside the stated coding range

    Minor

    Table 1 reports a country mean of 5 for the treatment recommendation tool, but country is coded 0 or 1. The same sample's country mean is reported as 0.5 for the feedback tool.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Lin, H-C, 2021, Educ Technol Soc, 24, 123
  2. 2022, R: A Language and Environment for Statistical Computing
  3. Lin, H-C, 2021, Educ Technol Soci, 24, 123

Cited by

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Related

Authors
Kleine, A.-K., Kokje, E., Lermer, E., & Gaube, S.
Affiliations
  • Ludwig-Maximilians-Universität München (DE)
  • Technische Hochschule Augsburg (DE)
Corresponding author
Kleine, Anne-Kathrin
ORCID
Venue
JMIR Human Factors
Publisher
JMIR Publications Inc.
Published
2023-07-12
Volume
10
Pages
e46859
ISSN
2292-9495 (ISSN-L)
DOI
10.2196/46859
PMC
PMC10372564
OpenAlex
W4376612788
Semantic Scholar
307fb054b09eb660046df3fc3195317762d4248a
Cited by
49
References
53
Citations by year
2026: 17 · 2025: 19 · 2024: 10 · 2023: 3
FWCI
12.58
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Human Factors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence adoption, mental health services, intention to use, Unified Theory of Acceptance and Use of Technology, psychotherapists, psychology students, perceived usefulness, social influence, perceived ease of use, trust in AI, AI anxiety, technology readiness, treatment decision support

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