Vol. 2026 No. 1 (2026): 2026 Continuous Issue
Articles

A Conceptual Model of Institutional Adaptation: Developmental Trajectories of AI Integration in Medical Education

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  • Angellar Manguvo,
  • Benford Mafuvadze
Angellar Manguvo
University of Missouri-Kansas City
Benford Mafuvadze
Unaffiliated
Bio
DOI: https://doi.org/10.46787/ijaipil.v2026i1.6964

Published 2026-03-24 — Updated on 2026-03-26

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Keywords

  • Curriculum Transformation, Pedagogical Change, Faculty Preparedness, AI, Medical Education, Cognitive Offloading

How to Cite

Manguvo, A., & Mafuvadze, B. (2026). A Conceptual Model of Institutional Adaptation: Developmental Trajectories of AI Integration in Medical Education. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2026(1). https://doi.org/10.46787/ijaipil.v2026i1.6964 (Original work published March 24, 2026)

Copyright (c) 2026 Angellar Manguvo, Benford Mafuvadze

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Artificial intelligence (AI) is increasingly reshaping how biomedical knowledge is accessed, synthesized, and applied, raising important questions about the competencies that medical education seeks to cultivate. Although often framed as unprecedented, these developments can be situated within a broader history of technological change in education, in which new tools have reconfigured relationships among knowledge, expertise, and learning. This study adopts a historical conceptual analyses approach to examine how medical education may respond to AI by drawing insights from an earlier technological transition: the introduction of handheld calculators in mathematics education. Using a structured comparative analysis informed by institutional adaptation theory, the study traces how educational systems have engaged with technological disruption over time, including patterns of institutional resistance, pedagogical repositioning, gatekeeping practices, curricular reconsideration, assessment debates, and governance responses. The calculator transition is considered not as a direct analogue to AI, but as a historical case that illuminates recurring dynamics of institutional change. The analysis informs the articulation of a developmental trajectory model that characterizes how medical education systems may adapt as AI becomes integrated into medical training and clinical learning environments. The model is presented as an interpretive framework for understanding institutional adaptation rather than as a predictive schema. Situating contemporary debates within broader historical patterns, the study offers a theoretically informed perspective on how medical education engages with technological change. It underscores the value of integrating historical insight with conceptual analysis to better understand ongoing transformations in professional education.

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