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Ethics and Technology in Nursing Education: Rethinking Artificial Intelligence.

Palmira Canales Morales¹

¹Nurse, Ph.D Candidate. Professor, Universidad Santo Tomás, Sede Santiago, Chile. palmiracanalesmo@santotomas.cl.

Investigacion Y Educacion En Enfermeria

|November 25, 2025

Summary

Artificial intelligence (AI) in nursing education offers inclusion and reduced gaps but risks surveillance and exclusion. Ethical integration requires careful design, regulation, and pedagogical focus on care, dignity, and critical thinking.

Keywords:

artificial intelligence education, nursing ethics, nursing health equity

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Area of Science:

Nursing Education
Artificial Intelligence Ethics
Digital Health

Background:

The integration of artificial intelligence (AI) into nursing professional education presents complex ethical considerations.
Recent scientific literature highlights both opportunities and risks associated with AI in this domain.

Purpose of the Study:

To critically analyze the ethical challenges and opportunities of AI in nursing education.
To examine AI's role in personalized learning, reducing educational disparities, and enhancing clinical judgment.
To address risks like algorithmic surveillance and technological exclusion.

Main Methods:

Argumentative review of recent scientific literature.
Analysis of key dimensions: personalized learning, digital equity, and AI-assisted simulations.
Examination of ethical risks including algorithmic surveillance and mechanization of moral reasoning.

Main Results:

AI presents opportunities for personalized learning, inclusion, and academic autonomy in nursing education.
AI can potentially reduce educational gaps through policies ensuring digital equity.
AI-assisted simulations can strengthen clinical and ethical judgment, but risks of surveillance and exclusion exist.

Conclusions:

The ethical integration of AI in nursing education hinges on its design, regulation, and pedagogical application, prioritizing care, dignity, and critical thinking.
Addressing technological exclusion in low digital literacy contexts is crucial.
Further research is needed to guide regulatory frameworks and educational strategies for ethical AI implementation.