Generative-AI: Your Creative Partner to Build Simulations for Dietetics Practice, Education, and Interprofessional Training

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Generative artificial intelligence (AI) is rapidly transforming how we teach, learn, and practice in health professions education. While simulation-based learning is foundational in training dietitians and other health professionals, creating realistic scenarios can be time- and resource-intensive. This interactive session will highlight innovative ways generative AI can be used to create dynamic simulations that enhance experiential learning and skill development.

Presenters will showcase examples of AI-powered simulations used in dietetics education and interprofessional training, including the Motivational Interviewing Counseling Buddy, the Leadership Lab, and an interprofessional healthcare system featuring AI-generated patients, clinicians, and leaders. These tools create immersive environments for practicing counseling, communication, and decision-making.

Product Publish Details

CPEU: 1.50 (Valid Until September 15, 2029)

Learning Objectives

Describe how generative AI can be used to design and facilitate realistic simulations that enhance experiential learning in dietetics and interprofessional education.
Demonstrate how good prompt design influences the outputs of AI-generated simulations to create accurate and engaging educational content.
Evaluate approaches for integrating AI simulations ethically and effectively into health professions education to support communication, leadership, and critical thinking skill development.

Performance Indicators

6.1.2 Applies understanding of informatics terminology and input and output devices (e.g., food and nutrition management systems, electronic health record (EHR), relational databases, wearables, cloud platforms, AI-driven tools).
10.4.7 Demonstrates competent use of technology to enhance learning experiences and the delivery of information.
6.3.5 Understands emerging technologies (e.g., AI, machine learning) and associated ethical considerations, including bias, data ownership, transparency, and the responsible use of data by healthcare professionals or clients.

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