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Reconstructing the General Computing Curriculum for Medical Students through Computational Thinking and Artificial Intelligence Literacy

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• Volume 13,Issue 5, Sept. 2026

Year:

2026

Type of Publication:

Article

Keywords:

Artificial Intelligence Literacy, Computational Thinking, Computing Education, Curriculum Design, Medical Education

Authors:

• Wang, Ning

Journal:

IJIRES

Volume:

13

Number:

5

Pages:

60-67

Month:

September

ISSN:

2349-5219

BibTex:

@article{2099, author = "Ning Wang", abstract = "General computing courses in medical universities must respond to artificial intelligence (AI) while preserving the concepts that make data and digital systems understandable. This conceptual study aims to develop a feasible reconstruction framework that integrates computational thinking, AI literacy, medical relevance, and responsible use into an inherited general computing curriculum for medical students. The framework was developed through an interpretive analysis of an anonymised curriculum source base comprising a 171-page legacy lesson plan, ten chapter-based courseware files, and a teaching calendar, together with relevant scholarship on computational thinking, AI literacy, constructive alignment, and medical AI education. The analysis identified four recurrent curriculum tensions: competition between obsolete and contemporary content, uneven medical contextualisation, addition rather than integration of AI, and assessment dominated by recall and software operation. In response, a one-core, four-module, three-level framework is proposed. Its core is responsible computational problem solving in medicine; the four modules are computational thinking, medical data foundations, medical AI applications, and responsible AI and innovation; and learning progresses from recognition to application and design. A recurring seven-step medical problem chain embeds interpretation, verification, and ethical reflection within technical work. A simulated electronic medical record database task further demonstrates how the framework can be implemented with existing laboratory resources and without real patient data. The study therefore provides a coherent and practically implementable design logic for gradual curriculum renewal, while its learning effectiveness remains to be established through future classroom evaluation.", issn = "23495219", journal = "IJIRES", keywords = "Artificial Intelligence Literacy, Computational Thinking, Computing Education, Curriculum Design, Medical Education", month = "September", number = "5", pages = "60-67", title = "{R}econstructing the {G}eneral {C}omputing {C}urriculum for {M}edical {S}tudents through {C}omputational {T}hinking and {A}rtificial {I}ntelligence {L}iteracy", volume = "13", year = "2026", }

Abstract:

General computing courses in medical universities must respond to artificial intelligence (AI) while preserving the concepts that make data and digital systems understandable. This conceptual study aims to develop a feasible reconstruction framework that integrates computational thinking, AI literacy, medical relevance, and responsible use into an inherited general computing curriculum for medical students. The framework was developed through an interpretive analysis of an anonymised curriculum source base comprising a 171-page legacy lesson plan, ten chapter-based courseware files, and a teaching calendar, together with relevant scholarship on computational thinking, AI literacy, constructive alignment, and medical AI education. The analysis identified four recurrent curriculum tensions: competition between obsolete and contemporary content, uneven medical contextualisation, addition rather than integration of AI, and assessment dominated by recall and software operation. In response, a one-core, four-module, three-level framework is proposed. Its core is responsible computational problem solving in medicine; the four modules are computational thinking, medical data foundations, medical AI applications, and responsible AI and innovation; and learning progresses from recognition to application and design. A recurring seven-step medical problem chain embeds interpretation, verification, and ethical reflection within technical work. A simulated electronic medical record database task further demonstrates how the framework can be implemented with existing laboratory resources and without real patient data. The study therefore provides a coherent and practically implementable design logic for gradual curriculum renewal, while its learning effectiveness remains to be established through future classroom evaluation.

Full text: IJIRES_2099_FINAL.pdf Online version [Bibtex]