Integration of artificial intelligence in clinical nursing training: challenge from an equity perspective

Abstract

Introduction: Artificial intelligence (AI) has become an emerging tool in higher health education, especially in clinical nursing training. Its integration raises ethical challenges related to educational equity, data privacy, and the potential dehumanization of care. Materials and methods: A mixed-methods exploratory study (predominantly qualitative) was conducted at the Metropolitan University of Ecuador, with a purposive, non-probability sample of 20 nursing students. An 11-item structured questionnaire (closed and one open-ended question) was administered, divided into four sections: knowledge, ethical perceptions, applications, and expectations. The data were processed using theoretical methods (analysis-synthesis, induction-deduction). Results: 60% of the students possess basic knowledge about AI, but only 45% use it effectively (mainly for information retrieval). 70% expressed concern about the lack of privacy and the use of personal data; 20% believed that AI could generate inequities in academic evaluation. 90% requested faculty training on ethical risks. 85% requested an institutional code of ethics. 80% believe that AI should complement teaching; 75% recognize its potential to reduce geographical disparities through remote clinical simulation. Discussion: The findings are consistent with previous studies that identify AI as a complementary, not a substitute, tool for clinical judgment, and highlight concerns about unequal access and data protection. Conclusions: The integration of AI into clinical training requires clear institutional regulations, specialized training in digital ethics and critical literacy, ensuring equitable access and preserving the humanistic values of the profession.