Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy

Abstract Background The increasing prevalence of mental health disorders, compounded by a global shortage of psychotherapists, highlights the need for innovative solutions such as Artificial Intelligence (AI) or Machine Learning (ML) applications. These technologies have demonstrated potential in di...

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Main Authors: Julian Wagner, Anna-Sophia Schwind
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Psychology
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Online Access:https://doi.org/10.1186/s40359-025-03071-7
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author Julian Wagner
Anna-Sophia Schwind
author_facet Julian Wagner
Anna-Sophia Schwind
author_sort Julian Wagner
collection DOAJ
description Abstract Background The increasing prevalence of mental health disorders, compounded by a global shortage of psychotherapists, highlights the need for innovative solutions such as Artificial Intelligence (AI) or Machine Learning (ML) applications. These technologies have demonstrated potential in diagnostics, treatment personalization, and therapy optimization. However, their integration into psychotherapeutic practice requires understanding psychotherapists’ attitudes toward AI/ML, which remains underexplored. This study aims to investigate these attitudes, focusing on factors influencing AI acceptance and perceived usefulness. Methods A cross-sectional survey was conducted among 181 licensed psychotherapists in Germany, recruited via the German Psychotherapeutical Association’s online directory. The survey assessed attitudes toward AI/ML, technical affinity, and perceptions of AI’s utility across psychotherapeutic tasks. Hierarchical regression analyses were used to identify predictors of AI acceptance. Results Positive attitudes toward AI/ML were significantly predicted by its perceived usefulness in conducting diagnoses and creating personalized treatment plans. Empathic support, while rated lower in terms of enhancing therapy, was still a significant predictor across all groups. Technically affine therapists associated AI with benefits in diagnostics, whereas non-affine therapists emphasized empathic support and relapse prediction. Conclusion Negative attitudes toward AI/ML application are discussed in a frame of fears of professional replacement and limited understanding of AI/ML technologies. Overall, 40% of the sample self-identified as not technically inclined, suggesting a knowledge gap in AI/ML that might influence attitudes. Education could emerge as a critical factor in addressing fears and uncertainties surrounding AI/ML. Emphasizing the irreplaceable human qualities of psychotherapists may also alleviate fears of obsolescence.
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spelling doaj-art-2bdaf99e1f9541c98fb99bd36df1da8e2025-08-20T03:42:02ZengBMCBMC Psychology2050-72832025-07-0113111510.1186/s40359-025-03071-7Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapyJulian Wagner0Anna-Sophia Schwind1Department of Psychology, University of TrierDepartment of Applied Psychology, University of Applied Sciences Ravensburg-WeingartenAbstract Background The increasing prevalence of mental health disorders, compounded by a global shortage of psychotherapists, highlights the need for innovative solutions such as Artificial Intelligence (AI) or Machine Learning (ML) applications. These technologies have demonstrated potential in diagnostics, treatment personalization, and therapy optimization. However, their integration into psychotherapeutic practice requires understanding psychotherapists’ attitudes toward AI/ML, which remains underexplored. This study aims to investigate these attitudes, focusing on factors influencing AI acceptance and perceived usefulness. Methods A cross-sectional survey was conducted among 181 licensed psychotherapists in Germany, recruited via the German Psychotherapeutical Association’s online directory. The survey assessed attitudes toward AI/ML, technical affinity, and perceptions of AI’s utility across psychotherapeutic tasks. Hierarchical regression analyses were used to identify predictors of AI acceptance. Results Positive attitudes toward AI/ML were significantly predicted by its perceived usefulness in conducting diagnoses and creating personalized treatment plans. Empathic support, while rated lower in terms of enhancing therapy, was still a significant predictor across all groups. Technically affine therapists associated AI with benefits in diagnostics, whereas non-affine therapists emphasized empathic support and relapse prediction. Conclusion Negative attitudes toward AI/ML application are discussed in a frame of fears of professional replacement and limited understanding of AI/ML technologies. Overall, 40% of the sample self-identified as not technically inclined, suggesting a knowledge gap in AI/ML that might influence attitudes. Education could emerge as a critical factor in addressing fears and uncertainties surrounding AI/ML. Emphasizing the irreplaceable human qualities of psychotherapists may also alleviate fears of obsolescence.https://doi.org/10.1186/s40359-025-03071-7Applications of artificial intelligencePsychotherapistBarriersImplementation
spellingShingle Julian Wagner
Anna-Sophia Schwind
Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
BMC Psychology
Applications of artificial intelligence
Psychotherapist
Barriers
Implementation
title Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
title_full Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
title_fullStr Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
title_full_unstemmed Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
title_short Investigating psychotherapists’ attitudes towards artificial intelligence in psychotherapy
title_sort investigating psychotherapists attitudes towards artificial intelligence in psychotherapy
topic Applications of artificial intelligence
Psychotherapist
Barriers
Implementation
url https://doi.org/10.1186/s40359-025-03071-7
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