PURPOSE
Faculty development plans are widely required in medical education, yet
many are generic, difficult to operationalize, and poorly aligned with
educators’ needs—particularly for community-based faculty with limited access
to institutional resources. This project aims to design and implement an
AI-assisted Individualized Faculty Development Plan (IFDP) system that uses
retrieval-augmented GPT to generate tailored, standards-aligned plans based
on educators’ self-identified competencies, interests, and contextual
constraints.
METHODS
Guided by Kern’s model, we identified gaps in access and relevance and
conducted targeted needs assessments with community clinical sites. Drawing
on self-regulation and self-determination theories, we developed a
competency-based intake synthesizing key domains such as bedside teaching,
assessment, and learning environment. Educators rate confidence, commitment,
and contextual barriers, and intake data feed into a retrieval-augmented GPT
agent that generates personalized goals, suggested activities, timelines, and
metrics. Recommendations are mapped to institutional priorities and national
educator standards, and curated resources from local, national, and scholarly
materials inform the educational strategies. The output is an actionable IFDP
usable for reviews, onboarding, and promotion processes.
RESULTS
Pilot testing with faculty and educational team members showed that the
AI-assisted IFDP system produces specific, standards-aligned development
plans. Participants reported that plans were more relevant, actionable, and
tailored to their teaching contexts than traditional FDPs. Plan evaluations
demonstrated coherence among intake data, educator standards, and outputs,
with most plans containing clear objectives, feasible activities, and
measurable timelines. Curated resources were rated as appropriate and useful,
and administrators reported improved consistency in documentation for faculty
reviews.
CONCLUSIONS
The IFDP provides a scalable, context-sensitive model for individualized
faculty development. Strengths include competency alignment, personalization,
and adaptability to diverse settings. Limitations include reliance on
self-report, varying levels of faculty acceptance of AI tools, and
institutional requirements for data and security compliance.