Evidence map›Paper›PMID 42171999›Full record

ArticleMedical education online2026

Measuring AI literacy in medical students: scale development and validation within a self-determination theory framework.

Hung-Che Lin, Chin-Sheng Lin, Ching Sing Chai, Chin Lin, Pei-Jan Tsai, Jyh-Chong Liang

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical education online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hung-Che LinDepartment of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan; Department of Otolaryngology, School of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan.
Chin-Sheng LinDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.
Ching Sing ChaiDepartment of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong Kong, Hong Kong.
Chin LinSchool of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan.
Pei-Jan TsaiDepartment of Medical Education, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.
Jyh-Chong LiangProgram of Learning Sciences and Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, Taiwan.ORCID 0000-0002-2423-5950

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly integrated into healthcare, making AI literacy an essential competency for medical students. Existing assessments are often generic, lack validation in medical education, and are not grounded in learning theory. This study developed and validated the AI Literacy Scale for Medical Students (ALSMS) within a self-determination theory (SDT) framework.

methodsWe used a split-sample validation design (

resultsEFA identified nine factors organized into the SDT domains of competence, relatedness, and autonomy. CFA supported the correlated nine-factor structure and demonstrated strong psychometric properties. Model comparisons identified two theory-consistent, well-fitting solutions: a correlated nine-factor model and an SDT-aligned second-order model with

conclusionsThis study provides initial validity evidence for interpreting ALSMS scores as indicators of medical students' AI literacy within an SDT-informed framework. The findings highlight the significance of integrating ethics into autonomy-supportive curricula and underscore the potential utility of ALSMS for curriculum design, advising, and the evaluation of AI literacy initiatives in medical education.

Indexed as

Artificial IntelligencePersonal AutonomyStudents, MedicalFactor Analysis, StatisticalFemaleHumansMalePsychometricsReproducibility of ResultsArtificial intelligenceliteracymedical educationself-determination theory (SDT)structural equation modeling (SEM)

Identifiers

PMID42171999
PMCPMC13202685

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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.