A Novel Cognitive Behavioral Therapy–Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Digital 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. OBJECTIVE: This mixed methods study examines the feasibility of Socrates 2.0. METHODS: On 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. RESULTS: Recruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited. CONCLUSIONS: The 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».