Artificial intelligence (AI) technologies are increasingly integrated into nursing education often framed as neutral tools that enhance learning efficiency, consistency, and clinical decision-making. However, this instrumental framing obscures how AI may fundamentally reshape nursing students' perception, ethical orientation, and professional formation, particularly in developing country contexts. Nursing training in developing countries is often characterised by infrastructural constraint and epistemic inequality compared to developed countries. This paper offers a conceptual-theoretical analysis of AI in nursing education with a focus on how it mediates the ways nursing students come to see, interpret, and relate to patients and clinical knowledge. Drawing on postphenomenology and a reflexive decolonial stance, the paper conceptualises AI as a mediating force. The analysis is structured around four modes of technological mediation (embodiment, hermeneutic, alterity, and background relations) to examine how AI reshapes embodied clinical practice, interpretive frameworks, pedagogical authority, and the hidden curriculum of nursing education in developing country settings. Reflexive attention to moments of critical friction reveals tensions between the standardised logic embedded in AI systems and the situated, relational realities of nursing education. The paper argues that AI mediates not only what nursing students know, but how they learn to attend, judge, and assume ethical responsibility. By foregrounding mediation and epistemic justice, it calls for more reflexive, context-sensitive approaches to integrating AI in global nursing education.
Gideon Dzando (Wed,) studied this question.