AI-supported learning environments in nursing education: A scoping review of accessibility, engagement, and educational inclusion

Abstract

The integration of artificial intelligence across higher education has significantly reshaped instructional design, learner interaction, and pedagogical infrastructures within professional programs. In nursing education, AI-supported systems have been increasingly adopted to enhance instructional responsiveness, personalize learning experiences, and optimize feedback mechanisms. Despite expanding scholarly interest, existing research remains conceptually dispersed, with limited synthesis examining how AI-mediated environments intersect with accessibility, learner engagement, and broader inclusion-oriented concerns. 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. A comprehensive search was conducted across multidisciplinary databases, capturing empirical, conceptual, and review-based studies relevant to AI applications within nursing and health professions education. Extracted data were analyzed using a thematic mapping approach emphasizing conceptual patterns rather than methodological appraisal. Findings reveal that AI-supported learning environments are predominantly framed through three interrelated dimensions: accessibility-enhancing infrastructures, engagement-mediating mechanisms, and inclusion–ethical considerations. The literature suggests that adaptive systems, multimodal delivery, and algorithmically driven personalization may reduce structural learning barriers while supporting learner variability. However, engagement outcomes appear context dependent, influenced by pedagogical design, digital literacy, and learner perceptions of AI credibility. Importantly, the synthesis identifies persistent tensions concerning algorithmic bias, equity implications, governance structures, and ethical safeguards. These patterns informed the development of an emergent conceptual model positioning AI-mediated nursing education as a multidimensional ecosystem rather than a purely technological intervention. The review highlights critical knowledge gaps and underscores the necessity of aligning technological innovation with inclusive pedagogical frameworks and institutional ethical accountability. Collectively, the study contributes to a more coherent understanding of AI’s pedagogical, structural, and equity-related implications within nursing education.