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Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence
Sponsor: University Hospital, Basel, Switzerland
Summary
The study assesses the potential of using computational models, specifically large language models, to simulate psychotherapeutic sessions, aiming to improve therapy outcomes and advance therapist training through innovative technology.
Official title: Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence: A Proof-of-Concept Study of In Silico Psychotherapy Research
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
520
Start Date
2025-02-01
Completion Date
2027-01-27
Last Updated
2025-02-10
Healthy Volunteers
No
Conditions
Interventions
High Levels of Common Therapeutic Factors
The therapist large language model (LLM) is designed to show high levels of empathy, warmth, and genuineness. This setup aims to create a supportive and trusting therapeutic environment to improve patient engagement. High levels of these positive factors are linked to better psychotherapy outcomes and a stronger therapist-patient relationship.
Low Levels of Common Therapeutic Factors
The therapist LLM for this group is designed to show low levels of empathy, warmth, and genuineness. This setup aims to examine how a less supportive and empathetic therapist affects psychotherapy sessions. Lower levels of these positive behaviors can lead to reduced patient engagement and a weaker therapist-patient relationship, potentially hindering therapy outcomes.
Standard motivational interviewing
Motivational interviewing techniques as applied during the sessions on which the transcripts are based.
Locations (1)
University Hospital Basel
Basel, Switzerland