arXiv:2606.17786v1 Announce Type: cross Abstract: Training psychotherapists in evidence-based interventions such as Acceptance and Commitment Therapy (ACT) requires repeated practice with meaningful feedback, yet opportunities for safe, standardized training are limited by ethical, logistical, and resource constraints. We introduce a system designed to support ACT-oriented psychotherapy training through spoken dialogue with an embodied virtual patient. The system uses large language models to simulate patient behavior conditioned on profiles derived from real therapy sessions and configurable
Advances in large language models (LLMs) and conversational AI have reached a point where they can simulate complex human interactions realistically enough for specialized training applications like psychotherapy.
This development addresses critical limitations in current psychotherapy training, offering a scalable, ethical, and standardized method for practitioners to gain experience and feedback.
Psychotherapy training can become more accessible and efficient, potentially leading to a broader base of well-trained mental health professionals capable of delivering evidence-based interventions.
- · Mental health training institutions
- · AI-driven simulation platform developers
- · Psychotherapy trainees
- · Patients needing therapy
- · Traditional role-playing training methods
- · Training programs constrained by limited resources
Psychotherapy students will have access to on-demand, realistic practice with patient avatars.
The quality and consistency of psychotherapy training will improve, leading to more effective therapists.
Increased availability of skilled psychotherapists could ultimately improve mental health outcomes on a population level.
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