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

## TL;DR

This paper proposes a trustworthy framework for integrating AI into nursing exams, combining insights from Taiwan and Japan to ensure fairness, transparency, and quality.

## Contribution

A cross-border governance blueprint for AI-augmented OSCEs in nursing education, emphasizing human oversight and international alignment.

## Key findings

- A 5-AI-role model is mapped across OSCE workflows with human-in-the-loop judgment.
- Taiwan and Japan contribute complementary strengths in agile development and policy scaffolding.
- Four governance pillars are distilled into implementable techniques for auditability and quality assurance.

## 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.