Evidence map›Paper›PMID 42712849›Full record

ArticlePRiMER (Leawood, Kan.)2026

Exploring Faculty Perspectives on Using AI to Support Individualized Learning Plan Goal-Writing.

Karen Isaacs, Nicole Freund, Nicole Delgado-Salisbury, Tana Chongsuwat, Kathleen Young

Abstract read
In one paragraph

Article in PRiMER (Leawood, Kan.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Karen IsaacsPrisma Health Family Medicine Residency Program, Greer, SC.
Nicole FreundDepartment of Family and Community Medicine, University of Kansas School of Medicine, Wichita, KS.
Nicole Delgado-SalisburyNew York Medical College at Saint Joseph's Family Medicine Residency, Yonkers, NY.
Tana ChongsuwatNorthwestern McGaw Family Medicine Residency at Humboldt Park, Chicago, IL.
Kathleen YoungNovant Health NHRMC Family Medicine Residency, Wilmington, NC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencecompetency-based medical educationindividualized learning plan

Identifiers

PMID42712849
PMCPMC13550770

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.