Introduction to Fundamental AI and Ethical Considerations: A Didactic and Interactive Case-Based Session for Preclerkship Medical Students.
6 affiliations
- 1Assistant Dean for Preclinical Curriculum, Department of Medicine University of Texas Health San Antonio, Long School of Medicine.
- 2Director of the Synthesis Curriculum, Department of Medicine, University of Texas Health at San Antonio, Long School of Medicine.
- 3Clinical Skills Module Co-Director, Department of Medicine, University of Texas Health San Antonio, Long School of Medicine.
- 4Medicine, Behavior, and Society Module Co-Director, Department of Medicine, Division of Palliative Medicine, University of Texas Health San Antonio, Long School of Medicine.
- 5Medicine, Behavior, and Society Module Co-Director, Executive Director of the Academy of Educational Scholars, Department of Urology, Department of Medical Education, University of Texas Health San Antonio, Long School of Medicine.
- 6Director of the MD/MS in AI Dual Degree Program, Department of Urology, Department of Medical Education, University of Texas Health San Antonio, Long School of Medicine.
Abstract
Introduction
Widespread integration of artificial intelligence (AI) into health care systems and evolving policies are altering how we must teach medical students. Fundamental AI knowledge and the ethical and responsible use of AI are currently suggested competency domains for health care professionals. Moreover, ethical AI disclosure is incorporated into developing institutional and state policies. Addressing this curricular gap, we created a session to teach preclinical medical students essential AI terminology, prompting strategies, and ethical considerations for AI use in patient care.Methods
We implemented a 1-hour didactic lecture followed by a 2-hour case-based learning session in the 2025-2026 Medicine, Behavior, and Society module for 236 first-year medical students. We used a postsession, retrospective survey to assess students' perceived knowledge of the learning objectives. We used the Wilcoxon signed rank test (\ualpha = .05) to analyze survey response data. We developed a 5-question multiple-choice quiz (MCQ) to assess students' postsession knowledge and performed a psychometric item analysis.Results
Ninety-seven (41.1%) participating students completed the postsession survey and quiz. Postsession perceived knowledge improved across all objectives, with a large (r > 0.50) effect size on a 5-point Likert scale. The mean (SD) postsession MCQ score was 3.77 (1.02), with discrimination indices ranging from 0.154 to 0.692.Discussion
This combined didactic and case-based session delivers timely instruction on AI fundamentals and ethics for preclinical learners. The session significantly increased perceived knowledge, equipping students for future ethical and responsible AI use during clerkships.
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BibTeX
@article{cunningham2026introduction,
title = {{Introduction to Fundamental AI and Ethical Considerations: A Didactic and Interactive Case-Based Session for Preclerkship Medical Students.}},
author = {John M Cunningham and Sadie Trammell Velasquez and Luyang Jin and Jason Morrow and Sylvia Botros-Brey and Ronald Rodriguez},
journal = {MedEdPORTAL : the journal of teaching and learning resources},
year = {2026},
month = oct,
doi = {10.15766/mep_2374-8265.11645},
url = {https://europepmc.org/article/MED/42920877},
note = {CC BY},
}
RIS
TY - JOUR
TI - Introduction to Fundamental AI and Ethical Considerations: A Didactic and Interactive Case-Based Session for Preclerkship Medical Students.
AU - John M Cunningham
AU - Sadie Trammell Velasquez
AU - Luyang Jin
AU - Jason Morrow
AU - Sylvia Botros-Brey
AU - Ronald Rodriguez
PY - 2026
DA - 2026/10/23
JO - MedEdPORTAL : the journal of teaching and learning resources
DO - 10.15766/mep_2374-8265.11645
UR - https://europepmc.org/article/MED/42920877
AB - Introduction Widespread integration of artificial intelligence (AI) into health care systems and evolving policies are altering how we must teach medical students. Fundamental AI knowledge and the ethical and responsible use of AI are currently suggested competency domains for health care professionals. Moreover, ethical AI disclosure is incorporated into developing institutional and state policies. Addressing this curricular gap, we created a session to teach preclinical medical students essential AI terminology, prompting strategies, and ethical considerations for AI use in patient care.Methods We implemented a 1-hour didactic lecture followed by a 2-hour case-based learning session in the 2025-2026 Medicine, Behavior, and Society module for 236 first-year medical students. We used a postsession, retrospective survey to assess students' perceived knowledge of the learning objectives. We used the Wilcoxon signed rank test (\ualpha = .05) to analyze survey response data. We developed a 5-question multiple-choice quiz (MCQ) to assess students' postsession knowledge and performed a psychometric item analysis.Results Ninety-seven (41.1%) participating students completed the postsession survey and quiz. Postsession perceived knowledge improved across all objectives, with a large (r > 0.50) effect size on a 5-point Likert scale. The mean (SD) postsession MCQ score was 3.77 (1.02), with discrimination indices ranging from 0.154 to 0.692.Discussion This combined didactic and case-based session delivers timely instruction on AI fundamentals and ethics for preclinical learners. The session significantly increased perceived knowledge, equipping students for future ethical and responsible AI use during clerkships.
SN - 2374-8265
ER -