The rapid rise of generative artificial intelligence (GenAI) has created powerful new instructional opportunities alongside major pedagogical questions. While tools like ChatGPT can enhance lesson planning, case scenario design, and assessment development, many medical educators face a steep learning curve. Without structured guidance, integrating these fast-evolving and context-sensitive tools into high-stakes, discipline-specific medical curricula carries significant risks.
Key Takeaways |
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Combatting Cognitive Deskilling: To prevent cognitive deskilling, GenAI should be used as an assistive tool for critique and reflection rather than as a substitute for human judgment. |
| Structured Experiential Training: The PDGenAI-P series uses a five-workshop framework to scaffold faculty from basic prompt development to independent, discipline-specific application. |
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Faculty Perspective Shift: Following the training, faculty shifted from viewing GenAI as a potential replacement to seeing it as an interactive copilot that augments their clinical teaching expertise. |
In a recent pilot study published in JMIR Medical Education, researcher Rajalakshmi Anand and colleagues at Weill Cornell Medical College-Qatar tackle this training gap. They describe the design and evaluation of a specialized professional development workshop series, offering a flexible and theory-informed roadmap for training medical faculty in responsible AI use.
In academic medicine, instructional decisions carry direct professional accountability and long-term implications for patient care. If educators rely on automated GenAI outputs without critical appraisal, inaccuracies or oversimplifications could inadvertently shape a student's clinical reasoning or professional development.
Furthermore, over-automating complex instructional tasks poses a threat known as cognitive deskilling. When experienced educators delegate high-level pedagogical reasoning entirely to an algorithm, their own active decision-making and instructional expertise risk being diminished over time. To avoid this, faculty development must position GenAI explicitly as an assistive resource for critique and reflection, rather than a substitute for human judgment.
To support medical educators, the research team developed the Professional Development in Generative Artificial Intelligence for Pedagogy (PDGenAI-P) series. Grounded in Experiential Learning Theory and the Technological Pedagogical Content Knowledge (TPACK) framework, the series brought together a pilot cohort of ten medical faculty members.
Rather than focusing purely on technical operations, the program utilized a five-workshop interactive structure designed to scaffold prompt development, artifact generation, and critical reflection, in which faculty were guided through:
Workshop 1: Lesson Planning and Case Design – Utilizing tools like ChatGPT and Claude to construct foundational lesson blueprints and problem-based case scenarios.
Workshop 2: Assessment Development – Faculty drafted multiple-choice and USMLE-style questions linked to localized curricula.
Workshop 3: GenAI Imagery – Faculty explored visual generators like Adobe Firefly to build anatomical or clinical visualizations.
Workshop 4: Simulation Role-Play – Faculty designed interactive, text-based simulation role-plays for communication tracking.
Workshop 5: Independent Experimentation – Faculty independently applied tailored prompts to their own discipline-specific workflows.
Post-intervention surveys and two-week follow-up data showed high participant satisfaction and a marked boost in self-reported confidence. Qualitative analysis revealed that faculty discourse shifted organically from basic tool exploration toward deeper pedagogical reasoning, with participants viewing GenAI as an interactive copilot to augment, rather than replace, their clinical teaching expertise.
| In this video, Rajalakshmi Anand from Weill Cornell Medicine-Qatar presents a mixed methods pilot study on the design, implementation, and evaluation of a faculty development workshop series focused on integrating Generative Artificial Intelligence (GenAI) into medical education. |
Why JMIR?
The authors chose JMIR Medical Education to share these findings due to the journal's focus on digital transformations, medical informatics training, and emerging pedagogical technologies. As academic institutions worldwide race to establish policies for generative media, this study provides a concrete, adaptable blueprint for developing human-centered faculty training models that prioritize critical appraisal and professional accountability.
Curious to see how experiential workshops can reshape AI training in academic medicine? Watch the video featuring Rajalakshmi Anand and read the full pilot study to explore the prompt design frameworks, learning scaffolds, and the institutional roadmap for implementing responsible GenAI in medical education.