Evidence map›Paper›PMID 41818744›Full record

ArticleJMIR formative research2026

Trustworthy AI-Augmented Objective Structured Clinical Examinations in Nursing Education: Taiwan-Japan Viewpoint on 5 AI Roles, Governance, and Cross-Border Implementation.

Kazumi Kubota, Ayako Nishimura, Ryoma Seto, Liu Li

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Kazumi Kubota *Research Organization, Shimonoseki City University, Shimonoseki, Yamaguchi, Japan.ORCID 0000-0003-2270-2313
Ayako Nishimura *Faculty of Healthcare, Division of Nursing, Tokyo Healthcare University, Tokyo, Japan.ORCID 0000-0002-6981-1667
Ryoma Seto *Faculty of Healthcare, Division of Healthcare Informatics, Tokyo Healthcare University, Tokyo, Japan.ORCID 0000-0002-6154-9342
Liu Li *Show Chwan Health Care System, Taipei, Taiwan.ORCID 0000-0002-4800-644X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (AI) is arriving in high-stakes assessment; however, governance, validity evidence, and faculty readiness remain uneven. From a Taiwan-Japan perspective, we outline a pragmatic, transferable approach to integrating AI into nursing objective structured clinical examinations (OSCEs) using a 5-AI-role model-learning assistant, AI‑augmented standardized patient, assessment assistant, case generator, and learning analyst-mapped across pre-OSCE, peri-OSCE, and post-OSCE workflows with human-in-the-loop final judgment. Taiwan contributes agile interdisciplinary development, staged pilots (practice, mock OSCE, and limited high-stakes stations), A/B comparisons, and explainability-by-design logging that links scores to time-stamped evidence. Japan contributes robust policy scaffolding (national AI use guidance in K-12, a revised nursing model core curriculum with outcomes and assessment blueprints, and institutional research cultures that support auditability and quality assurance). We distill 4 cross-cutting governance pillars-human oversight, learning process transparency, ethics and safety, and traceability-into implementable techniques (machine-readable rubrics, standardized patient persona cards, bias monitoring, and targeted faculty development). Aligning with international principles (International Advisory Committee for AI; Organisation for Economic Co-operation and Development; United Nations Educational, Scientific and Cultural Organization; World Health Organization; European Commission's High Level Expert Group; and National Institute of Standards and Technology), we propose a joint road map and shared registry to benchmark reliability, validity, equity, and workload impact. This viewpoint targets OSCE directors, nursing educators, and institutional leaders and provides a phase-gated governance blueprint rather than reporting original trial outcomes. Taiwan-led agility, complemented by Japan's standards-driven assurance, can form an Asia-Pacific reference model for trustworthy AI‑augmented OSCE in nursing education.

Indexed as

Artificial IntelligenceEducational MeasurementEducation, NursingHumansJapanTaiwanAIartificial intelligenceassessment governancegenerative AIgenerative artificial intelligencenursing educationobjective structured clinical examinationOSCEstandardized patientTaiwan-Japan collaborationtransparency and traceability

Identifiers

PMID41818744
PMCPMC13022535

What OpenQuestion holds

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LicenceCC BY
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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.