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Transcript-derived patient profiles conditioning virtual patient behavior across seven dimensions

Design · All designs
Evidence · no claims cited

Description

Four anonymized patient profiles (two male, two female) were synthesized with GPT-5.2 from 4–7 real ACT sessions each, which "averaged 58–92 therapist-patient turn pairs and lasted 38–60 minutes". A structured prompt extracted stable patterns across seven dimensions (patient overview, core difficulties, values and motivations, emotional and cognitive themes, behavioral patterns, progress trajectory, simulation hooks) and constrained the model to rely only on transcript content and produce anonymized descriptions.

Design Implications

Context

Requirements

  • Real therapy transcripts as source material
  • Joint analysis of multiple sessions to capture stable patterns

Constraints

  • Profiles are anonymized approximations derived only from transcript content

Target Learners

  • Psychotherapists in training

Learning Goals

  • Clinically plausible patient interaction practice

Claims

Examples

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Key Sources

  • Pascal Riachi, Sofie Kamber, Stella Brogna, Andrew Gloster, Rafael Wampfler. (2026). Toward Accessible Psychotherapy Training Using AI-Driven Interactive Patient Avatars. https://arxiv.org/abs/2606.17786
Page metadata
Field Value
type design
id transcript-derived-virtual-patient-profiles
title Transcript-derived patient profiles conditioning virtual patient behavior across seven dimensions
description Four anonymized patient profiles (two male, two female) were synthesized with GPT-5.2 from 4–7 real ACT sessions each, which "averaged 58–92 therapist-patient turn pairs and lasted 38–60 minutes".
status review
generated by process:settle-candidates on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — pascal-riachi-2026

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