TherapyTrainer: Using AI to train therapists in written exposure therapy

Though evidence-based treatments for mental disorders are effective, existing implementation efforts are expensive and difficult to scale. Novel solutions— especially those that offer active learning strategies, repeat skill practice and personalized feedback to therapists — are needed to fill this gap. We developed TherapyTrainer, which uses large language models (LLMs) to allow therapists to practice delivering written exposure therapy (WET) for PTSD to AI-Patients while receiving expert feedback from an AI-Consultant. Here we present initial feasibility, acceptability and usability data for TherapyTrainer gathered from therapists, supervisors, and WET expert-consultants across iterative rounds of development. In Phase 1, we rapidly prototyped and developed TherapyTrainer based on ongoing feedback from WET clinicians and experts (n = 4). In Phase 2, mixed methods data from therapists engaged in an otherwise-routine WET workshop (n = 14) indicated that TherapyTrainer is feasible and acceptable and may help therapists feel prepared to deliver WET. In Phase 3, therapists (n = 6) completed structured user testing interviews to identify key issues impacting usability for subsequent rounds of development. AI and large language models hold potential to provide ongoing support to therapists in a cost-effective and scalable manner, and may help close the research-practice gap.