More intelligent faculty development: Integrating GenAI in curriculum development programs
Abstract
EDUCATIONAL CHALLENGE: Generative Artificial Intelligence (GenAI) has rapidly emerged as a potentially transformative tool in education. Faculty development (FD) programs, particularly in curriculum development (CD), are ideal settings for incorporating GenAI to benefit faculty and their learners. However, concerns about accuracy, bias, and ethical implications necessitate structured and responsible integration. SOLUTION AND IMPLEMENTATION: We incorporated GenAI across five CD programs at Johns Hopkins University (JHU) in 2023-2024. We developed structured exercises using customizable prompts aligned with each step of the Six-Step Approach for Curriculum Development in Medical Education and encouraged learners to critically engage with GenAI during required exercises and assignments. LESSONS LEARNED AND NEXT STEPS: Structured exercises encouraged experimentation, critical evaluation, and innovation. Participants reported increased efficiency and creativity. Role modeling, balanced messages about GenAI's capabilities and limitations, and multidisciplinary teamwork were key enablers of success. This pilot offers an example of integration of GenAI into existing FD programs without requiring additional time or sacrificing rigor in CD processes. By sharing our findings globally, we hope to democratize FD and contribute to the responsible, scalable adoption of GenAI in diverse educational contexts.
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Current faculty development opportunities are often offered either as one-off workshops/prompt-a-thons or as formalized multi-session programs requiring substantial institutional support and time commitment.
“Current faculty development opportunities are often offered at one of two ends: workshops [2] or “prompt-a-thons” that engage participants in brief one-off events [3] or formalized programs that weave genAI training into multi-session curricula requiring substantial institutional support and time commitment [4].”
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- Authors
- Khamis, N., Chen, B., Egan, C., Gaglani, S., & Tackett, S.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Khamis, Nehal
- Venue
- Medical Teacher
- Publisher
- Informa UK Limited
- Published
- 2025-03-03
- Volume
- 47
- Issue
- 11
- Pages
- 1739-1741
- ISSN
- 0142-159X (ISSN-L)
- 1466-187X
- OpenAlex
- W4408102678
- Semantic Scholar
- 238b84b964784a012816e36c54b5f1ef330d05a6
- Cited by
- 12
- References
- 4
- Citations by year
- 2026: 6 · 2025: 6
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Teacher (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Johns Hopkins University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- generative AI, faculty development, curriculum development, medical education, AI prompts, critical evaluation of AI, AI ethics, role modeling, multidisciplinary teamwork
- MeSH terms
- Artificial Intelligence, Curriculum, Education, Medical, Faculty, Medical, Humans, Staff Development, Program Development
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