Evidence map›Paper›PMID 41808119›Full record

ArticleBMC medical education2026

Exploring health care learners' perceptions of AI integration in the curriculum: a survey tool and findings.

Linda Chang, Maura Polansky, Darvin Yi, Martin MacDowell, Radhika Sreedhar, Dawn Mosher, Alan Schwartz

Abstract read
In one paragraph

Article in BMC medical education, 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

7 authors.

Linda ChangUniversity of Illinois Chicago Rockford, Rockford, USA. lchang@uic.edu.
Maura PolanskyUniversity of Illinois Chicago, Chicago, Illinois, USA.
Darvin YiUniversity of Illinois Chicago, Chicago, Illinois, USA.
Martin MacDowellUniversity of Illinois Chicago Rockford, Rockford, USA.
Radhika SreedharUniversity of Illinois Chicago, Chicago, Illinois, USA.
Dawn MosherSaint Anthony College of Nursing- Rockford, Rockford, Illinois, USA.
Alan SchwartzUniversity of Illinois Chicago, Chicago, Illinois, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFew studies have assessed students in different healthcare disciplines’ perceptions of the importance of learning standard competencies in AI technology. This study aimed to evaluate the learning needs, apprehensions/anxieties, and digital self-efficacy of students in medicine, nursing, and pharmacy regarding AI technology in healthcare.

methodsThis cross-sectional survey study included 521 students: 384 medical, 67 nursing, and 78 pharmacy students, with response rates of 32%, 61%, and 52% from the colleges of medicine, nursing, and pharmacy, respectively. Survey data were collected over a 3-month period using a self-reported questionnaire. Data analysis was conducted using SPSS, including descriptive statistics, ANOVA, and chi-square tests.

resultsStudents from all three disciplines agreed on the importance of learning about AI in healthcare, with agreement rates for the six core AI skill domains ranging from 80 to 92%. The AI Learning Anxiety/Fears questionnaire revealed varying anxiety levels about AI technology learning across the disciplines. Over 20% of students across all disciplines agreed or strongly agreed that they experience anxiety on the AI Learning Anxiety/Fears questionnaire. The digital self-efficacy scale score was negatively correlated with AI learning anxiety (r=-0.32, p = 0.01).

conclusionThis study offers insights into students’ perspectives on learning about AI, which will inform the development of effective AI curricula. These findings highlight the need for standardized AI curricula that can address learning needs and reduce anxiety across the healthcare disciplines.

Indexed as

CurriculumStudents, MedicalStudents, NursingStudents, PharmacyAdultAttitude of Health PersonnelCross-Sectional StudiesFemaleHumansMaleSelf EfficacySurveys and QuestionnairesYoung AdultAIAnxietyCore competenciesDigital self-efficacy scoreStudent perceptions

Identifiers

PMID41808119
PMCPMC13088441

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.