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.