National Psychology Training Consortium

Intermediate --- Builds upon the learner's foundational knowledge, familiarity with the literature and/or experience in a content area. Programming at this level includes more depth than at a beginning level program. It could also serve as a refresher course for individuals who have background in a content area and are interested in learning more contemporary applications.

Course Description and Target Audience

This half-day virtual training examines the responsible use of artificial intelligence (AI) in clinical supervision within Primary Care Behavioral Health (PCBH) and traditional psychology settings. Drawing on current research (2020–2026) in media psychology, digital health, and clinical decision-making, the presentation explores how AI tools influence diagnostic reasoning, case conceptualization, documentation, and supervisory processes. Participants will examine risks such as automation bias, misinformation, and overreliance on AI, as well as the implications for professional judgment, client care, and supervisee development. Through real-world case studies and applied scenarios, attendees will analyze how AI is currently being used in practice and identify appropriate supervisory responses. The training introduces practical, evidence-informed frameworks to help supervisors critically evaluate AI-generated content and guide trainees in its ethical and effective use. Interactive components, including breakout discussions, case analysis, and skills-based exercises, will support participants in applying concepts to real supervisory challenges. Emphasis is placed on ethical considerations such as confidentiality, informed consent, scope of competence, and accountability, equipping supervisors to integrate AI responsibly while maintaining high standards of clinical care. 

Target audience: Mental health professionals who provide clinical supervision to psychology interns in Primary Care Behavioral Health (PCBH) and traditional outpatient settings, including licensed psychologists and behavioral health supervisors. This training is designed for supervisors seeking to understand and guide the responsible use of artificial intelligence (AI) in clinical practice, supervision, and trainee development.

The content of this presentation is grounded in current, peer-reviewed research (2020–2026) from media psychology, digital health, behavioral science, and human-computer interaction (UX/UI), alongside established ethical guidelines in clinical practice and supervision. It incorporates both empirical evidence and lived experience from clinical practice and global health settings to illustrate how artificial intelligence (AI) shapes real-world decision-making, user behavior, and care delivery. The material reflects emerging research on AI’s benefits and limitations, including risks such as bias, misinformation, and impacts on professional judgment and autonomy. Evidence-informed frameworks are translated into practical supervisory strategies, enabling participants to critically evaluate AI tools, interpret user-facing design influences, and apply ethical decision-making in everyday clinical and supervisory contexts. Emphasis is placed on strengthening clinical judgment, enhancing supervisory effectiveness, and supporting responsible, human-centered integration of AI in practice.

The content of this presentation reflects the current and rapidly evolving state of research and practice related to artificial intelligence (AI) in behavioral health and supervision; as such, some evidence may become outdated as technologies and regulations continue to develop. While the training integrates interdisciplinary research, UX/UI considerations, and lived experience from clinical and global health contexts, there is a risk that concepts and applications may not generalize to all settings, populations, or emerging AI tools. The presentation focuses on conceptual frameworks and applied strategies rather than technical instruction on specific AI systems, and it does not provide legal guidance; participants are encouraged to consult organizational policies and jurisdiction-specific regulations. Case examples are illustrative and may not capture the full complexity of all clinical scenarios. Additionally, variability in participants’ familiarity with AI may influence the depth of application during the training. The training will also incorporate participants’ experiences and discussion to enhance relevance, which may result in variability in emphasis and takeaways across groups.

There are minimal risks associated with this training. Participants may experience mild discomfort when reflecting on ethical dilemmas, limitations in current practice, or uncertainties related to the use of artificial intelligence (AI) in clinical and supervisory contexts. Additionally, as the training introduces emerging technologies, some participants may feel uncertainty or cognitive overload when engaging with unfamiliar concepts. These risks are expected to be minimal and will be mitigated through a respectful, inclusive, and structured learning environment that supports open discussion and professional reflection.

This presentation intentionally incorporates diversity, equity, and inclusion by examining how artificial intelligence (AI) systems reflect and can amplify existing social, cultural, and structural biases that affect diverse populations. Content includes discussion of algorithmic bias, disparities in data representation, and the impact of AI-driven tools on marginalized groups, including differences in language, culture, socioeconomic status, and access to care. Case examples and scenarios are designed to reflect diverse client populations and both Primary Care Behavioral Health (PCBH) and traditional settings, ensuring relevance across practice contexts. The training also integrates lived experience and global perspectives to highlight how cultural context shapes both technology use and clinical interpretation. Participants are encouraged to critically evaluate AI outputs through a culturally responsive lens and to consider how supervision can support equitable, person-centered care. Interactive discussions further invite participants to reflect on their own identities, assumptions, and supervisory roles in promoting ethical and inclusive AI use in clinical practice.

Learning Objectives

• Participants will be able to describe how artificial intelligence (AI) tools influence clinical decision-making, case conceptualization, and supervisory processes in behavioral health settings.
• Participants will be able to identify common risks associated with AI use in supervision, including automation bias, misinformation, algorithmic bias, and impacts on professional judgment and autonomy.
• Participants will be able to analyze ethical considerations related to AI-assisted supervision, including confidentiality, informed consent, scope of competence, and accountability.
• Participants will be able to evaluate AI-generated clinical outputs using structured, evidence-informed frameworks to determine their appropriateness for clinical and supervisory use.
• Participants will be able to apply practical strategies to guide interns and practitioners in the responsible and ethical use of AI within Primary Care Behavioral Health (PCBH) and traditional psychology settings.
• Participants will be able to demonstrate approaches to integrating AI into supervision while maintaining clinical integrity, culturally responsive care, and adherence to professional standards.

Presenter Information

Dr. Tatyana El-Kour, PhD, is a psychologist and media psychology researcher with over 25 years of experience translating behavioral science, digital health, and ethics into practical guidance for clinicians and supervisors. She holds a PhD in Psychology with dual concentrations in Media Psychology and the Social Impact of Mobile and Immersive Technologies, and serves as a Senior Research Fellow at the Media Psychology Research Center. CITI-certified in human research and trained in advanced AI applications for data analysis, her work focuses on AI-mediated decision-making, algorithmic influence, and the ethical integration of technology into practice. She has presented on AI in psychology at multiple American Psychological Association conventions, including sessions on AI’s impact on clinical judgment and professional practice. Dr. El-Kour is known for her engaging, highly applied teaching style, equipping supervisors to critically evaluate AI, guide trainees responsibly, and navigate ethical decision-making in rapidly evolving, technology-mediated care environments.

Video Homestudy Format

CE’s for this homestudy training will be earned through completing the following tasks:

• Watch the presentation video and review all provided documents in their entirety.
• Pass the post-test questionnaire with at least 80% correct.

A program evaluation form will be provided to all who registered following the training. Please be sure to complete this form since your feedback helps direct future CE programming from our organization.

Registration Cost

This cost includes video links, materials, and tests required to obtain CE’s. Your CE certificate will be made available to you upon successful completion of the training (80% or higher on exam).

If you have any questions or concerns, please contact Training Support - Ember Serencko at [email protected].

National Psychology Training Consortium (NPTC) is approved by the American Psychological Association to sponsor continuing education for psychologists. NPTC maintains responsibility for this program and its content.