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Unlocking Large Language Models in PT Education: Harnessing Gen-AI With Strategic Prompt Engineering
1Matthew Smith is a board-certified clinical specialist in orthopaedic physical therapy, fellow of the American Academy of Orthopaedic Manual Physical therapists, Instructor in the Department of Physical Therapy & Human Movement Sciences at Northwestern University Feinberg School of Medicine.
Abstract:
As technology rapidly evolves, generative artificial intelligence (AI) and large language models are valuable tools in physical therapy education. Through strategies like prompt engineering, faculty can streamline curriculum development, generate cases and assessments, and provide timely, personalized feedback. This growing skillset supports innovation in teaching, preparing educators to thrive in an ever-changing learning environment. Prompt engineering is an underutilized but integral skill to provide useable and effective outputs for curricular tasks. Methods such as the IDEA method (Include essential components, Develop clear prompts, Evaluate and refine, Accountability) can assist in driving innovation in physical therapy education and prepare future clinicians for our new AI-enhanced academic and health care environments. This graphic aims to give practical and implementable tools that will improve faculty utilization of this technology.
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