TL;DR
Findings indicate that ESP-EMI collaboration enables context-responsive course design aligned with professional communication demands, while AI tools facilitate language learning and task support.
This study employed a scoping review methodology guided by Arksey and O’Malley’s framework to systematically map and synthesize the evolving body of literature addressing AI-supported learning environments in nursing education, contributing to a more coherent understanding of AI’s pedagogical, structural, and equity-re...
INSPIRE integrates phase-based governance, iterative production, continuous quality assurance, and human-in-the-loop AI use in one workflow built for healthcare e-learning and formalizes the role of artificial intelligence across the lifecycle while retaining clinical and pedagogical accountability with human experts.
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
It is argued that open, reflective classroom discussions around ethical concerns and climate impacts of AI are as important as the positive clinical applications to endow students with the knowledge to make decisions that align with social justice and social accountability.
This study identifies role-differentiated AI competencies and proposes a corresponding curriculum scaffold for healthcare education and identifies a layered structure that translates these competencies into corresponding curriculum implications across the health professions education continuum.
A comprehensive pedagogical framework for teaching English for Specific Purposes (ESP) to medical students is put forward by integrating three essential dimensions of contemporary academic education: academic writing, intercultural competence, and research skills.
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