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Evaluation ViewAttendees 5

Technology/Digital Health
Session Type:: Symposia

Symposium 66 - Artificial Intelligence (AI) as a means of supporting evidence-based practices in psychological treatments

Sunday, November 23, 2025
2:00 PM - 3:00 PM CST
Location: Imperial 11, Level 4
CE: 1

Keywords: Technology / Mobile Health, Training / Training Directors, Competence
Level of Familiarity: Basic to Moderate
Recommended Readings: Stade, E., Stirman, S. W., Ungar, L. H., Yaden, D. B., Schwartz, H. A., Sedoc, J., ... & DeRubeis, R. (2023). Artificial intelligence will change the future of psychotherapy: A proposal for responsible, psychologist-led development.,   Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with Artificial Intelligence: Current trends and future prospects. Journal of medicine, surgery, and public health, 100099.,   Kopelovich, S. L., Slevin, R., Brian, R. M., Shepard, V., Baldwin, S. A., Ben-Zeev, D., ... & Imel, Z. (2025). Preliminary investigation of an artificial intelligence-based cognitive behavioral therapy training tool. Psychotherapy.,   Imel, Z. E., Pace, B., Pendergraft, B., Pruett, J., Tanana, M., Soma, C. S., ... & Atkins, D. C. (2024). Machine Learning–Based Evaluation of Suicide Risk Assessment in Crisis Counseling Calls. Psychiatric services, 75(11), 1068-1074.,   NARAYANAN, A., & KAPOOR, S. (2024). AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Princeton University Press. https://doi.org/10.2307/jj.14736606

Artificial intelligence (AI) continues to transform behavioral healthcare and offers new opportunities for enhancing evidence-based practice (EBP). This symposium will explore how researchers, educators, and content experts are collaborating to build AI-based tools to augment training, fidelity monitoring, and supervision of EBPs. Presenters will discuss their process of collaboration, creation, and distillation of whole EBPs to smaller, concrete skills that can be successfully identified by AI classification models and provide learners reliable and consistent feedback. Evidence will be presented regarding the development, validity, and reliability of these tools. Presenters will focus how these AI-based tools can support clinical decision-making, and enhance therapist training across key therapeutic domains, specifically Multicultural Orientation (MCO), Cognitive Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), and suicide prevention. The session will also feature demonstrations of these AI-assisted tools that support culturally responsive care, and automated assessment of skill practice.

The first presentation will describe the research conducted on MCO, a treatment emphasizing interpersonal, client-centered, identity-focused, and immediacy skills. The second and third presentations, CBT and DBT respectively, will discuss the importance of skill based practice for complex treatment protocols, and the challenge of choosing and creating didactic content for key foundational skills. The final presentation will discuss prior work conducted to develop models to detect risk assessments, how that work has been extended to training development, and the process of including common factor skills (e.g., active listening and empathy).

This symposium is designed for clinicians, researchers, and educators interested in integrating AI into psychological treatments while ensuring that technology remains a tool for enhancing, rather than replacing, human-centered care. Participants will gain practical skills in understanding the process of creating AI-based training of EBPs, be able to identify four different examples of how classification based AI-models can augment EBP training, and better understand innovative methods to enhance clinical effectiveness while maintaining ethical standards and therapeutic integrity. Participants will learn content that has the possibility to empower them to make choices about when and how to use technology to augment their practice, training, and supervision.

Learning Objectives:

At the end of this session, the learner will be able to:
  1. Identify four artificial intelligence-based tools that support four different evidence-based treatments.
  2. Improve clinicians' ability to identify valid AI-based methods of training and evaluating evidence based practice fidelity.
  3. Understand four different examples of how a evidence-based treatment can be solidified into training and development.
  4. Improve clinicians’ proficiency in evaluating and applying artificial intelligence-generated insights to patient care.
  5. Explain ethical considerations when incorporating AI-based training, feedback and supervision into clinical workflows.

Presentations:

  • 2:04 PM - 2:18 PM CST
    (SYM 66) An Artificial-intelligence-based Multicultural Practice Training Program for Therapists: Development and Preliminary Findings

    Speaker: Matteo Bugatti, Ph.D. (he/him/his) – Oregon State University

    Co-author: Jesse Owen, Ph.D. – University of Denver

  • 2:18 PM - 2:32 PM CST
    SYM 66) Increasing Scalability of Training and Performance Feedback Through Ai-based Tools

    Speaker: Torrey Creed, Ph.D. (she/her/hers) – University of Pennsylvania

    Co-author: Roisin Slevin, BS – Lyssn.io

    Co-author: Amber Calloway, Ph.D. (she/her/hers) – University of Pennsylvania

    Co-author: Brian Pace, PhD (he/him/his) – Lyssn.io

    Co-author: Jordan Pruett, PhD – Lyssn.io

    Co-author: Zac Imel, Ph.D. – Lyssn.io

    Co-author: David Atkins, PhD (he/him/his) – Lyssn.io

  • 2:32 PM - 2:46 PM CST
    (SYM 66) Role Play or Real Play? Development of a Machine Learning-based Course with Behavioral Rehearsal and Feedback for Clinicians Using DBT Strategies with Simulated Clients

    Speaker: Adam Carmel, Ph.D. – University of Washington, Seattle

  • 2:46 PM - 3:00 PM CST
    (SYM 66) Scaling Support: Ai-driven Innovations for 988 and Crisis Helplines

    Speaker: Christina S. Soma, Ph.D. (she/her/hers) – Lyssn.io

    Co-author: Zac Imel, Ph.D. – Lyssn.io

    Co-author: Brian Pace, PhD (he/him/his) – Lyssn.io

    Co-author: Elizabeth Burr, BA – protoCall Services, Inc.

    Co-author: Brad Pendergraft, LCSW – protoCall Services, Inc.

    Co-author: Michael Tanana, Ph.D. (he/him/his) – Lyssn.io, Inc.

    Co-author: David Atkins, PhD (he/him/his) – Lyssn.io