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LEAPPT: Leveraging gEnerative Artificial Intelligence-Pediatric Pulmonology Training. Curriculum and content
Alvin V Singh1, Erin Khan1, Mary Moffat2
1Division of Allergy, Immunology, Pulmonary and Sleep Medicine, Children's Mercy - Kansas City, University of Missouri-Kansas City, Kansas City, MO, United States.
ATS Scholar
|March 12, 2026
Summary
Pediatric pulmonology fellowship programs are enhancing curricula using artificial intelligence for detailed outlines and learning tools. These updates aim to improve didactic quality and boost board exam passing rates for trainees.
Area of Science:
- Medical Education
- Pediatric Pulmonology
- Artificial Intelligence in Healthcare
Background:
- Faculty feedback highlighted concerns regarding the quality and structure of didactics in a pediatric pulmonology fellowship.
- Previous curriculum redesigns necessitated further improvements to address educational needs.
Purpose of the Study:
- To enhance the pediatric pulmonology fellowship curriculum based on faculty feedback.
- To leverage generative artificial intelligence for curriculum development and improvement.
Main Methods:
- Implemented changes based on faculty survey feedback regarding didactic quality.
- Utilized generative artificial intelligence to create a detailed course content outline with specific objectives and study points.
- Developed testing questions, flipped classroom sessions, and evaluations.
Main Results:
- A comprehensive course content outline was generated using AI, serving as a foundational reference for learners.
- Ongoing curriculum enhancements include supplementary learning materials and assessment tools.
Conclusions:
- The revised curriculum incorporates AI-generated content and innovative teaching methods.
- These enhancements are expected to improve the quality of pediatric pulmonology fellowship training and address challenges with board certifying exam pass rates.
