# SCH: Training Mental Health Supporters with Virtual Patients and Automated Feedback

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2024 · $306,034

## Abstract

Under-treatment of mental health problems remains a major issue in the US, especially for youth, people
of color, and individuals with low incomes. Technology may help reduce disparities in and expand the
reach of mental health services. However, the newest technologies, such as generative AI, remain fraught
with perils such as hallucinations. Therefore, rather than using AI to directly interact with clients, we will
harness generative AI to provide training to mental health support providers, with the ultimate goal of
increasing accessibility of mental health services and improving mental health outcomes for those
receiving care. The goal of this research project is to develop and evaluate an automated, scalable
system for delivering experiential training to mental healthcare providers. Specifically, we propose to
develop a multi-agent training environment to provide interactive and experiential training on the micro-
skills and underlying common factors for both lay counselors and paraprofessionals. Our training
environment consists of three components: Virtual Patient, Assessor Agents, and Trainer Agents. Our
proposal has four aims. During Aim 1, we will develop and evaluate a set of LLM-based Virtual Patients
(VPs) that (a) realistically depict a wide range of common clinical problems (e.g., depression, job-related
stress, ADHD, and suicidality), (b) engage in coherent conversations with trainees, and (c) present typical
counseling challenges, such as addressing resistance to sharing problems in depth. During Aim 2, we will
develop an Assessor Agent capable of automatically assessing the micro-skills used by the trainee, as
well as how the trainee accomplishes higher-level segment goals (common factors). During Aim 3, we will
develop a Trainer Agent capable of interacting with trainees, the Assessor Agent module, and the Virtual
Patient module to achieve optimal training goals. During Aim 4, we will recruit 7Cups supporters to use
our multi-agent training environment to evaluate its impacts on the training outcomes.

## Key facts

- **NIH application ID:** 11062823
- **Project number:** 1R01MH139114-01
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** HOLLY A SWARTZ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $306,034
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11062823

## Citation

> US National Institutes of Health, RePORTER application 11062823, SCH: Training Mental Health Supporters with Virtual Patients and Automated Feedback (1R01MH139114-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11062823. Licensed CC0.

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