Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study
Abstract BackgroundDespite potential risks, artificial intelligence–based chatbots that simulate psychotherapy are becoming more widely available and frequently used by the general public. A comprehensive way of evaluating the quality of these chatbots is needed. O...
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JMIR Publications
2025-07-01
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| Series: | JMIR Formative Research |
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| author | Kunmi Sobowale Daniel Kevin Humphrey |
| author_facet | Kunmi Sobowale Daniel Kevin Humphrey |
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| collection | DOAJ |
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Abstract
BackgroundDespite potential risks, artificial intelligence–based chatbots that simulate psychotherapy are becoming more widely available and frequently used by the general public. A comprehensive way of evaluating the quality of these chatbots is needed.
ObjectiveTo address this need, we developed the CAPE (Conversational Agent for Psychotherapy Evaluation) framework to aid clinicians, researchers, and lay users in assessing psychotherapy chatbot quality. We use the framework to evaluate and compare the quality of popular artificial intelligence psychotherapy chatbots on the OpenAI GPT store.
MethodsWe identified 4 popular chatbots on OpenAI’s GPT store. Two reviewers independently applied the CAPE framework to these chatbots, using 2 fictional personas to simulate interactions. The modular framework has 8 sections, each yielding an independent quality subscore between 0 and 1. We used t
ResultsChatbots consistently scored highly on the sections of background information (subscores=0.83-1), conversational capabilities (subscores=0.83-1), therapeutic alliance, and boundaries (subscores=0.75-1), and accessibility (subscores=0.8-0.95). Scores were low for the therapeutic orientation (subscores=0) and monitoring and risk evaluation sections (subscores=0.67-0.75). Information on training data and knowledge base sections was not transparent (subscores=0). Except for the privacy and harm section (mean 0.017, SD 0.00; t3P
ConclusionsThe CAPE framework offers a robust and reliable method for assessing the quality of psychotherapy chatbots, enabling users to make informed choices based on their specific needs and preferences. Our evaluation revealed that while the popular chatbots on OpenAI’s GPT store were effective at developing rapport and were easily accessible, they failed to address essential safety and privacy functions adequately. |
| format | Article |
| id | doaj-art-c1fb6266d397480aaafcbb4a1895e832 |
| institution | DOAJ |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | JMIR Publications |
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| series | JMIR Formative Research |
| spelling | doaj-art-c1fb6266d397480aaafcbb4a1895e8322025-08-20T02:40:21ZengJMIR PublicationsJMIR Formative Research2561-326X2025-07-019e65605e6560510.2196/65605Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional StudyKunmi Sobowalehttp://orcid.org/0000-0002-3489-7114Daniel Kevin Humphreyhttp://orcid.org/0009-0005-3530-4279 Abstract BackgroundDespite potential risks, artificial intelligence–based chatbots that simulate psychotherapy are becoming more widely available and frequently used by the general public. A comprehensive way of evaluating the quality of these chatbots is needed. ObjectiveTo address this need, we developed the CAPE (Conversational Agent for Psychotherapy Evaluation) framework to aid clinicians, researchers, and lay users in assessing psychotherapy chatbot quality. We use the framework to evaluate and compare the quality of popular artificial intelligence psychotherapy chatbots on the OpenAI GPT store. MethodsWe identified 4 popular chatbots on OpenAI’s GPT store. Two reviewers independently applied the CAPE framework to these chatbots, using 2 fictional personas to simulate interactions. The modular framework has 8 sections, each yielding an independent quality subscore between 0 and 1. We used t ResultsChatbots consistently scored highly on the sections of background information (subscores=0.83-1), conversational capabilities (subscores=0.83-1), therapeutic alliance, and boundaries (subscores=0.75-1), and accessibility (subscores=0.8-0.95). Scores were low for the therapeutic orientation (subscores=0) and monitoring and risk evaluation sections (subscores=0.67-0.75). Information on training data and knowledge base sections was not transparent (subscores=0). Except for the privacy and harm section (mean 0.017, SD 0.00; t3P ConclusionsThe CAPE framework offers a robust and reliable method for assessing the quality of psychotherapy chatbots, enabling users to make informed choices based on their specific needs and preferences. Our evaluation revealed that while the popular chatbots on OpenAI’s GPT store were effective at developing rapport and were easily accessible, they failed to address essential safety and privacy functions adequately.https://formative.jmir.org/2025/1/e65605 |
| spellingShingle | Kunmi Sobowale Daniel Kevin Humphrey Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study JMIR Formative Research |
| title | Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study |
| title_full | Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study |
| title_fullStr | Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study |
| title_full_unstemmed | Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study |
| title_short | Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study |
| title_sort | evaluating the quality of psychotherapy conversational agents framework development and cross sectional study |
| url | https://formative.jmir.org/2025/1/e65605 |
| work_keys_str_mv | AT kunmisobowale evaluatingthequalityofpsychotherapyconversationalagentsframeworkdevelopmentandcrosssectionalstudy AT danielkevinhumphrey evaluatingthequalityofpsychotherapyconversationalagentsframeworkdevelopmentandcrosssectionalstudy |