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

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.

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Publication Details

Journal
JMIR Formative Research
Published
2026-03-12
DOI
https://doi.org/10.2196/87830
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

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

Ayako Nishimura, Ryoma Seto, Li Liu, Kazumi Kubota
JMIR Formative Research
Artificial Intelligence in Healthcare and Education
article

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

Ayako Nishimura, Ryoma Seto, Li Liu, Kazumi Kubota
article en

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.

JMIR Formative ResearchVol. 10
Shimonoseki City University (JP), Tokyo Health Care University (JP), University of Tokyo Hospital (JP), Chu Shang Show Chwan Hospital (TW)
Openalex Percentile: Top 28%
Artificial Intelligence in Healthcare and Education
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