Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence

NCT06813066 | Completed

• 1 other identifier: 0000-00000; th24Meinlschmidt
• Study Type: interventional
• Target: 520
• Locations: 1 country
• Sites: 1

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.

Conditions

Mental Disorder

Keywords

In silico Psychotherapy; Artificial intelligence (AI) applications; Motivational Interviewing

Outcome Measures

Primary Outcomes (1)

• Simulation's Accuracy in generating Psychotherapeutic Dialogues

  Assessment of the simulation's ability to accurately produce psychotherapeutic dialogues that adhere to the principles and techniques of motivational interviewing (MI), as determined by the average global scores of the Motivational Interviewing Treatment Integrity (MITI) code 4.2. The MITI code 4.2 includes various subscales, such as empathy and MI spirit, each scored on a scale from 1 to 5, with lower scores suggesting a need for improvement in MI delivery, while higher scores reflect stronger therapeutic skills and better patient outcomes.

  12 months

Secondary Outcomes (9)

• Number of Errors/Deviations

  12 months

• Metric of Verbal Content (Therapist)

  12 months

• Metric of Verbal Content (Patient)

  12 months

• Turn-takings

  12 months

• Improvement of Patient

  12 months

Study Arms (3)

High Levels of Common Therapeutic Factors

EXPERIMENTAL

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

Behavioral: High Levels of Common Therapeutic Factors

Low Levels of Common Therapeutic Factors

EXPERIMENTAL

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

Behavioral: Low Levels of Common Therapeutic Factors

Transcripts of real intervention sessions

OTHER

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

Behavioral: Standard motivational interviewing

Interventions

High Levels of Common Therapeutic Factors 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.

High Levels of Common Therapeutic Factors

Low Levels of Common Therapeutic Factors 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.

Low Levels of Common Therapeutic Factors

Standard motivational interviewing BEHAVIORAL

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

Transcripts of real intervention sessions

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• Simulation protocols with severe simulation errors

Sponsors & Collaborators

• University Hospital, Basel, Switzerland: lead
• RWTH Aachen University: collaborator
• University of Basel: collaborator
• University of Trier: collaborator

Study Sites (1)

University Hospital Basel

Basel, 4031, Switzerland

Location

MeSH Terms

Conditions

Mental Disorders

Study Officials

• Gunther Meinlschmidt, Prof. Dr.

  University Hospital and University of Basel

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 3, 2025
• First Posted: February 6, 2025
• Study Start: February 1, 2025
• Primary Completion: August 31, 2025
• Study Completion: August 31, 2025
• Last Updated: May 22, 2026

Record last verified: 2026-05

Locations

CH (1)