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Artificial Intelligence in Health

BRIEF REPORT
Feasibility of artificial intelligence-driven

personalized learning for internal medicine
residents: Integrating adaptive artificial

intelligence in flipped classrooms

2
2
Marcos A. Sanchez-Gonzalez * , Noelani-Mei Ascio , Omar Shah ,
1
Ashley Matejka 3 , Mark Terrell 4 , and Salman Muddassir 2
1 LECOM School of Health Services Administration, Bradenton, Florida, United States of America
2 Internal Medicine Program, HCA Florida Oak Hill Hospital, Brooksville, Florida, United States of
America
3 Research and Development, QHSLab, Inc., West Palm Beach, Florida, United States of America
4 Department of Medical Education, Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania,
United States of America

*Corresponding author:
Marcos A. Sanchez-Gonzalez Abstract
(msanchez-gonzalez@lecom.edu)
Citation: Sanchez-Gonzalez MA, Medical residency training faces persistent challenges in delivering individualized
Ascio N, Shah O, Matejka A, learning experiences. While flipped classroom models promote engagement,
Terrell M, Muddassir S. Feasibility they often lack real-time, personalized feedback. Artificial intelligence (AI)-driven
of AI-driven personalized learning
for internal medicine residents: platforms offer a promising solution by dynamically adapting content to residents’
Integrating adaptive AI in flipped evolving needs. This study evaluated the feasibility and effectiveness of integrating
classrooms. Artif Intell Health. adaptive AI beings into a flipped classroom model for internal medicine residents.
2025;2(4):139-145.
doi: 10.36922/AIH025130023 The AI-powered platform, edYOU, incorporated a personalized ingestion engine to
customize learning content and an intelligent curation engine to ensure content
Received: March 25, 2025 integrity. Residents interacted with AI beings capable of adjusting real-time content
1st revised: May 23, 2025 delivery based on performance and progress. Learning outcomes were assessed
2nd revised: May 30, 2025 using platform engagement metrics, simulation-based quiz results, and resident
feedback. Among eligible residents, 92% actively used the platform, spending an
3rd revised: June 4, 2025 average of 32.3 h (a few minutes to 148 h). A significant positive correlation was
4th revised: June 13, 2025 observed between time spent on the platform and quiz performance (r = 0.63,
Accepted: June 16, 2025 p<0.001), with 82.6% of educational topics engaged. Learners spent more time
on difficult content areas, highlighting the system’s ability to adapt to individual
Published online: July 16, 2025 challenges. Integrating AI into the flipped classroom proved feasible and was
Copyright: © 2025 Author(s). associated with improved engagement, learning efficiency, and academic
This is an Open-Access article performance. These results support using AI-enhanced educational tools to foster
distributed under the terms of the
Creative Commons Attribution tailored, learner-centered experiences in graduate medical education. Further
License, permitting distribution, research is warranted to optimize implementation strategies and evaluate the
and reproduction in any medium, long-term impact of AI-driven learning environments on resident development
provided the original work is
properly cited. and competency outcomes.
Publisher’s Note: AccScience
Publishing remains neutral with Keywords: Artificial intelligence; Personalized learning; Internal medicine; Flipped
regard to jurisdictional claims in
published maps and institutional classroom; Residency training; Medical education
affiliations.

Volume 2 Issue 4 (2025) 139 doi: 10.36922/AIH025130023