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Leveraging Large Language Models for Simulated Psychotherapy Client Interactions: Development and Usability Study of

Daniel Cabrera Lozoya1, Mike Conway1, Edoardo Sebastiano De Duro2

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Summary
This summary is machine-generated.

Large language models (LLMs) create realistic mental health client chatbots for training. While effective, psycholinguistic differences highlight areas for improvement in Client101.

Keywords:
chatbotsmedical educationmental healthpsychotherapy trainingvirtual client

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Area of Science:

  • Artificial Intelligence
  • Psychology
  • Clinical Training

Background:

  • Large language models (LLMs) demonstrate advanced human-like text generation capabilities.
  • LLMs offer potential applications in simulating clients for mental health training.
  • Client101 is a novel web platform utilizing LLM-driven chatbots for this purpose.

Purpose of the Study:

  • To develop and evaluate a web-based conversational psychotherapy training tool.
  • The tool features LLM-driven chatbots simulating clients with mental health conditions.
  • To assess the realism and training utility of these AI-powered clients.

Main Methods:

  • Developed GPT-4 powered chatbots simulating depression and generalized anxiety disorder.
  • Conducted 29 simulated therapy sessions with 16 mental health professionals.
  • Administered post-session surveys and analyzed psycholinguistic features using LIWC, comparing to human client data.

Main Results:

  • Surveys indicated high participant satisfaction with chatbot realism (93% for anxiety).
  • Significant psycholinguistic differences were found between chatbot and human transcripts in 3/8 anxiety and 4/9 depression features.
  • Key differentiating features included negations, family, negative emotions, feeling, and health/illness terms.

Conclusions:

  • GPT-4 chatbots show promise as effective mental health training tools.
  • Participant feedback validates their utility in simulating realistic client interactions.
  • Identified psycholinguistic variations provide specific targets for enhancing chatbot realism and training effectiveness.