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Leveraging Traditional and Generative Artificial Intelligence for Programmatic Decision Making in Faculty Development

Gayle D Haischer-Rollo1, Jessica T Servey, Bizualem Zelelew

  • 1Dr. Haischer-Rollo: Assistant dean for faculty development, associate professor, Department of Pediatrics, School of Medicine, Uniformed Services University, Bethesda, MD. Dr. Servey: Associate dean for faculty affairs, professor, Department of Family Medicine, School of Medicine, Uniformed Services University, Bethesda, MD. Ms. Zelelew: Data analyst II, Office of Faculty Affairs, School of Medicine, Uniformed Services University, Bethesda, MD. Dr. McFate: Director of faculty affairs, assistant professor, Department of Family Medicine, School of Medicine, Uniformed Services University, Bethesda, MD. Ms. Chi: Education coordinator II, Office of Faculty Affairs, School of Medicine, Uniformed Services University, Bethesda, MD. Dr. Duncan: Assistant dean for assessment, assistant professor, Department of Preventive Medicine and Biostatistics, School of Medicine, Uniformed Services University, Bethesda, MD.

The Journal of Continuing Education in the Health Professions
|June 10, 2026
PubMed
Summary

The AI-enhanced program evaluation (AIEPE) framework streamlines medical education assessment using AI for faster, deeper insights. This approach transforms data analysis, enabling better decision-making and continuous program improvement.

Keywords:
artificial intelligencelarge language modelsprogram evaluation

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