ArticleJMIR research protocols2024
A Novel Cognitive Behavioral Therapy-Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study.
Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "It's Not Only Attention We Need": Systematic Review of Large Language Models in Mental Health Care.JMIR mental health · 2025Pooled it
- AI Agents Are Coming: 5-Stage Taxonomy of Language-Based AI Systems for Psychiatry, Psychotherapy, and Counseling.JMIR mental health · 2026Article
- Artificial intelligence in traumatic stress treatment: The TRUST framework for ethical development, clinical applications, and research advancement.Journal of traumatic stress · 2026Review
- Comparing researcher-curated AI-drafted feedback styles for negative automatic thought believability: an online randomized scenario-based experiment.Frontiers in psychology · 2026Article
- AI-Facilitated Cognitive Reappraisal via Socrates 2.0: Mixed Methods Feasibility Study.JMIR mental health · 2025Article
- A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework.JMIR mental health · 2025Article
- Generative artificial intelligence in posttraumatic stress disorder treatment: Exploring five different use cases.Journal of traumatic stress · 2025Article
- TherapyTrainer: Using AI to Train Therapists in Written Exposure Therapy.Cognitive and behavioral practice · 2025Article
- Aligning large language models for cognitive behavioral therapy: a proof-of-concept study.Frontiers in psychiatry · 2025Article
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6 authors.
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Abstract
backgroundDigital mental health tools, designed to augment traditional mental health treatments, are becoming increasingly important due to a wide range of barriers to accessing mental health care, including a growing shortage of clinicians. Most existing tools use rule-based algorithms, often leading to interactions that feel unnatural compared with human therapists. Large language models (LLMs) offer a solution for the development of more natural, engaging digital tools. In this paper, we detail the development of Socrates 2.0, which was designed to engage users in Socratic dialogue surrounding unrealistic or unhelpful beliefs, a core technique in cognitive behavioral therapies. The multiagent LLM-based tool features an artificial intelligence (AI) therapist, Socrates, which receives automated feedback from an AI supervisor and an AI rater. The combination of multiple agents appeared to help address common LLM issues such as looping, and it improved the overall dialogue experience. Initial user feedback from individuals with lived experiences of mental health problems as well as cognitive behavioral therapists has been positive. Moreover, tests in approximately 500 scenarios showed that Socrates 2.0 engaged in harmful responses in under 1% of cases, with the AI supervisor promptly correcting the dialogue each time. However, formal feasibility studies with potential end users are needed.
objectiveThis mixed methods study examines the feasibility of Socrates 2.0.
methodsOn the basis of the initial data, we devised a formal feasibility study of Socrates 2.0 to gather qualitative and quantitative data about users' and clinicians' experience of interacting with the tool. Using a mixed method approach, the goal is to gather feasibility and acceptability data from 100 users and 50 clinicians to inform the eventual implementation of generative AI tools, such as Socrates 2.0, in mental health treatment. We designed this study to better understand how users and clinicians interact with the tool, including the frequency, length, and time of interactions, users' satisfaction with the tool overall, quality of each dialogue and individual responses, as well as ways in which the tool should be improved before it is used in efficacy trials. Descriptive and inferential analyses will be performed on data from validated usability measures. Thematic analysis will be performed on the qualitative data.
resultsRecruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited.
conclusionsThe development of Socrates 2.0 and the outlined feasibility study are important first steps in applying generative AI to mental health treatment delivery and lay the foundation for formal feasibility studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58195.
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