Developing a Postgraduate Program for AI in Medicine with Kern’s Six-Step Curriculum Development Approach in Singapore

Source publication / research team or educational organization described in paper

Singapore

Researchers, educators, instructors, or facilitators as described in the source publication

• Higher education
• Professional / adult learning

Course implementation or course design

Not specified in extracted text

Not specified in extracted text

Ethics / responsible AI

• Teacher readiness, time, support, and classroom integration may affect implementation quality.

Graduate / professional, Adult / workforce

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Artificial Intelligence (AI) has rapidly transformed the medi- cal field, necessitating significant changes in medical educa- tion to prepare healthcare professionals for future work re- quirements.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Ethics / responsible AI

Not specified in extracted text

• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Teacher professional development, Ethics / responsible AI education.
• AI capability focus: Ethics / responsible AI.

• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.

• Case classified under: Published empirical study.
• Pedagogical pattern: Hands-on / experiential learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• However, the integration of AI into medical cur- ricula has been slow and lacks standardization.

• However, the integration of AI into medical cur- ricula has been slow and lacks standardization.

Artificial Intelligence (AI) has rapidly transformed the medi- cal field, necessitating significant changes in medical educa- tion to prepare healthcare professionals for future work re- quirements. However, the integration of AI into medical cur- ricula has been slow and lacks standardization.

• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Ethics and responsible use
• Teacher readiness
• Curriculum / course design
• Learning tool / resource design
• Teacher professional development
• Ethics / responsible AI education
• Ethics / responsible AI

• Completed

Graduate / professional, Adult / workforce

Higher education, Professional / adult learning

Ethics / responsible AI

Hands-on / experiential learning

Medium

Low to Medium

Developing a Postgraduate Program for AI in Medicine with Kern’s Six-Step Curriculum Development Approach in Singapore

• Chang Cai
• Michelle Jong
• Yih Yng Ng
• Jo-Anne Elizabeth Manski-Nankervis
• Kum Ying Tham
• Preman Rajalingam
• Boon Keong Ang
• Jennifer Anne Cleland
• Joseph Sung
• Xiuyi Fan

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35167

https://ojs.aaai.org/index.php/AAAI/article/view/35167

https://ojs.aaai.org/index.php/AAAI/article/view/35167/37322

004_Developing a Postgraduate Program for AI in Medicine with Kern#U2019s Six-Step Curriculum Development Approach in Singapore.pdf

9

Artificial Intelligence (AI) has rapidly transformed the medi- cal field, necessitating significant changes in medical educa- tion to prepare healthcare professionals for future work re- quirements. However, the integration of AI into medical cur- ricula has been slow and lacks standardization. In this paper, we present our work in developing a year-long postgraduate- level AI in Medicine program offered by a medical school at a public university in Singapore. Our curriculum design follows Kern’s six-step approach to medical curriculum de- velopment, organized into a four-session framework. These sessions involved collaboration with hospital and university administrators, educators, industry experts, and healthcare professionals. The program is structured around three core courses: Foundational Healthcare AI, Clinical Applications of Healthcare AI, and Governance and Ethics for Healthcare AI. Each course comprises multiple modules with associated projects, emphasizing hands-on learning. The program adopts a problem-based learning approach, supported by a blended learning environment to accommodate the schedules of work- ing healthcare professionals. Evaluations by industry experts highlight the program’s potential to address critical gaps in the healthcare sector. This study contributes to the integra- tion of AI into medical training by providing a standardized approach that can be adapted globally.

• Higher education
• Professional / adult learning

• Teacher readiness, time, support, and classroom integration may affect implementation quality.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• group_size
• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.