Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence

NCT ID: NCT06813066

Last Updated: 2025-02-10

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 520 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-02-01
• Study Completion Date: 2027-01-27

Brief 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.

Detailed Description

Health research has evolved significantly, increasingly incorporating computational models that improve our understanding and effectiveness of medical interventions. This shift from traditional to computational methods represents a major advancement in medical research, offering a more sustainable and innovative approach for conceptual advances and therapeutic discovery. In silico models, based on scientific simulation, use computational algorithms to mimic real-world systems or processes. This virtual environment allows researchers to explore phenomena impractical, unethical, dangerous, expensive, or impossible to study otherwise.

Psychotherapy is widely acknowledged as a primary treatment for a variety of mental health conditions, from depression and anxiety to personality disorders, offering significant pathways to recovery and improved quality of life. Yet current methods have shown limited effectiveness, prompting a need for innovative research approaches. In silico psychotherapy research leverages computational simulations, large language models (LLMs), and generative artificial intelligence to explore and refine psychotherapeutic interventions. By simulating human-like conversations, this approach provides insights into therapy dynamics and holds promise for revolutionizing therapist training and expanding treatment techniques.

This study aims to establish a proof-of-concept for simulating psychotherapeutic sessions using LLMs, focusing specifically on motivational interviewing. It involves the simulation of 512 psychotherapy sessions using LLMs as well as 8 real-world psychotherapy transcripts. By modeling human interactions, the study seeks to enhance healthcare delivery, therapist training, and personalized psychotherapy.

Conditions

Mental Disorder

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: SINGLE

Study Groups

High Levels of Common Therapeutic Factors

In this group, the patient-large language model (LLM) interacted with a therapist-LLM prompted to exhibit high levels of positive common factors.

Group Type: EXPERIMENTAL

High Levels of Common Therapeutic Factors

Intervention Type: BEHAVIORAL

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

In this group, the patient-large language model (LLM) interacted with a therapist-LLM prompted to exhibit low levels of positive common factors.

Group Type: EXPERIMENTAL

Low Levels of Common Therapeutic Factors

Intervention Type: BEHAVIORAL

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.

Transcripts of real intervention sessions

This group consists of published transcripts of real intervention sessions, in which motivational interview techniques have been applied.

Group Type: OTHER

Standard motivational interviewing

Intervention Type: BEHAVIORAL

Motivational interviewing techniques as applied during the sessions on which the transcripts are based.

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.

Intervention Type: BEHAVIORAL

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.

Intervention Type: BEHAVIORAL

Standard motivational interviewing

Motivational interviewing techniques as applied during the sessions on which the transcripts are based.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Simulation of psychotherapy sessions of conversations between an adult person presenting with a mental or behavioral health problem and a psychotherapist using large language models and 8 real-world transcripts

Exclusion Criteria

• Simulation protocols with severe simulation errors

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Trier University

UNKNOWN

Sponsor Role: collaborator

RWTH Aachen University

OTHER

Sponsor Role: collaborator

University of Basel

OTHER

Sponsor Role: collaborator

University Hospital, Basel, Switzerland

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Gunther Meinlschmidt, Prof. Dr.

Role: PRINCIPAL_INVESTIGATOR

University Hospital and University of Basel

Locations

University Hospital Basel

Basel, , Switzerland

Countries

Switzerland

Other Identifiers

0000-00000; th24Meinlschmidt

Identifier Type: -

Identifier Source: org_study_id