@article{10.22454/PRiMER.2026.443890,
author = {Isaacs, Karen M. and Freund, Nicole and Delgado-Salisbury, Nicole and Chongsuwat, Tana and Young, Kathleen M.},
title = {Exploring Faculty Perspectives on Using AI to Support Individualized Learning Plan Goal-Writing},
journal = {PRiMER},
volume = {10},
year = {2026},
month = {8},
doi = {10.22454/PRiMER.2026.443890},
abstract = {Introduction: Individualized Learning Plans (ILPs) are a key component for resident-centered learning and competency-based medical education in residency programs. Although best practices exist for ILPs, programs and faculty offer varied levels of support, and residents differ in their goal-writing experience, ability, and motivation. To address these disparities, this pilot study sought insight from family medicine residency faculty on the feasibility of using generative artificial intelligence (AI) to assist residents in writing ILP SMARTIE goals.
Methods: This pilot study surveyed faculty helping residents with ILPs at six family medicine residency programs across the United States. An exploratory qualitative approach was used to assess faculty perspectives on using AI to support ILP goal-writing.
Results: Eleven faculty participated, with comments suggesting that integrating AI into ILP goal-writing was feasible and improved goal structure and clarity, though it was not a replacement for faculty guidance. Some identified challenges like the need for appropriate prompt engineering and reviewing AI output for needed adjustments.
Conclusions: Faculty perspectives highlight the potential of using AI to reduce the burden of creating SMARTIE goals for higher quality ILPs, opening the door for future study on this topic.},
URL = {https://journals.stfm.org//primer/2026/isaacs-0055/},
eprint = {https://journals.stfm.org//media/wf1bepia/primer-2026-0055.pdf},
}