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Technical Demonstrations

Artificial Intelligence and Technology-based Interventions

Technical Demonstration 4 - Practical AI for Providers: Adding Large Language Models to the CBT Toolkit

Thursday, June 25, 2026
11:30 AM - 1:00 PM PDT
Location: Yerba Buena Salon 4, B3 Level
Earn 1.5 Credit
Keywords: Technology / Mobile Health, Education and Training,
Level of Familiarity: Basic to Moderate
Recommended Readings: American Psychological Association. (2025). Ethical Guidance for AI in the Professional Practice of Health Service Psychology. https://www.apa.org/topics/artificial-intelligence-machine-learning/ethical-guidance-professional-practice.pdf,   Bernstein, E. E., Jaroszewski, A. C., Jacoby, R. J., Bailen, N. H., Ragan, J., Usmani, A., & Wilhelm, S. (2025). Feasibility of using ChatGPT to generate exposure hierarchies for treating obsessive-compulsive disorder. Behavior Therapy, 56(4), 680–688. https://doi.org/10.1016/j.beth.2025.02.005,   Calvin, A., Lenhart, A., Hasse, A., Mann, S., and Robb, M.B. (2025). Teens, trust, and technology in the age of AI: Navigating trust in online content. San Francisco: CA: Common Sense Media.,   ,  

    Author(s)

  • Hannah Brody, Psy.D.

    Postdoctoral Fellow
    Hassenfeld Children’s Hospital at NYU Langone Medical Center
    New York, New York, United States

    • Co-Author(s)

  • Sandra S. Pimentel, Ph.D.

    Chief, Child & Adolescent Psychology
    Montefiore Medical Center
    Bronx, New York, United States

The release of ChatGPT in November 2022 highlighted the far-reaching potential of artificial intelligence (AI) and large language models (LLMs) across industries. In the years since, use of AI among psychologists is on the rise, with reportedly 71% of psychologists exploring the technology at least once in their practice (American Psychological Association, 2025). Yet, as AI utilization increases, so do concerns; leading fears include data breaches and security, unanticipated social harms, and biased outputs (APA, 2025).

The purpose of this training is to increase familiarity with concrete clinical use-cases for LLMs within cognitive behavioral therapy (CBT), address concerns in the AI age, and provide guidance regarding risks associated with AI use by providers and patients alike. This technical demonstration emphasizes hands-on, practice-oriented applications, with a particular focus on child and family mental health providers. Content will address what is known about rising adolescent use of generative AI (Madden et al., 2024), how clinicians can provide developmentally appropriate guidance to families, and ways AI tools can be thoughtfully and intentionally incorporated into treatment.

This demonstration will model live, structured examples of how AI can be integrated into CBT processes, including building fear-hierarchies (Bernstein et al., 2025), supporting problem-solving, enhancing psychoeducation, and strengthening rapport and engagement through clinician prompting and implementation guidance.

The demonstration will have four core components: 1) a brief, accessible overview of LLMs and response generation; 2) live demonstrations of practical CBT-relevant use cases; 3) guidance on youth AI use, including emerging concerns related to chatbot companions; and 4) discussion of risk mitigation strategies for ethical and responsible integration of AI into clinical practice.

Content will synthesize current APA ethical guidance, emerging empirical literature, and clinical examples. This training was evaluated as part of a pilot study of acceptability and utility at Montefiore Medical Center and was associated with improvements in provider-reported comfort and confidence regarding AI use and integration into clinical care.

Learning Objectives:

  • Identify appropriate clinical CBT use-cases for LLMs and describe how AI-generated responses are produced.
  • Use AI to generate CBT tools (e.g., fear hierarchies, psychoeducation) while maintaining clinical judgment and ethical standards.
  • Recognize the role and importance of discussing AI use with youth and families, including key risks and concerns (e.g., chatbots, isolation).
  • Implement basic risk-mitigation steps for ethical AI use in clinical practice.