AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.4903
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PERSPECTIVE ARTICLE

Contextualizing algorithmic literacy framework for global health workforce education

Seble Frehywot1,2†* ,  Yianna Vovides3†
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1 Department of Global Health, School/Faculty, George Washington University, Washington, District of Colombia, United States of America
2 Department of Health Policy and Management, School/Faculty, George Washington University, Washington, District of Colombia, United States of America
3 Centers in New Design in Learning and Scholarship, Georgetown University, Washington, District of Colombia, United States of America
AIH, 4903 https://doi.org/10.36922/aih.4903
Submitted: 22 September 2024 | Accepted: 11 November 2024 | Published: 28 November 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

With the rapid and accelerating advancement of generative artificial intelligence (AI), research is lagging on how to ensure that the health workforce becomes and stays AI-literate. This paper describes a way forward specifically toward establishing an AI-augmented curriculum within global health workforce education. By global health workforce education, we refer to the academic staff or faculty and students in medicine, nursing, global public health, and other health science fields. AI, unlike other technological advancements, is constantly changing. Therefore, the adoption of specific tools for health workforce education has to be analyzed in the context of the educational setting for shaping a sustainable and equitable AI-augmented global health workforce curriculum. This necessitates an integration of AI algorithmic literacy within academic curricula. In this paper, we propose the algorithmic literacy framework (ALF) for global health workforce education to address individual and organizational readiness. At the individual level, ALF examines one’s knowledge of and skills needed to implement AI within the context of their respective health education expertise. At the organizational level, ALF examines readiness across five areas: infrastructure and support systems, institutional support, Information and Communications Technology technical expertise, student engagement, faculty engagement, and analytics technical expertise. ALF offers universities and health workforce training institutions a way of organizing their approach to algorithmic/AI literacy readiness that embraces their organization’s values and, at the same time, urging them to act.

Keywords
Artificial intelligence
Algorithmic literacy
Medical education
Health sciences education
Global public health
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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