Cyberethics in nursing education: Ethical implications of artificial intelligence
Open accessAbstract
As the use of artificial intelligence (AI) technologies, particularly generative AI (Gen AI), becomes increasingly prevalent in nursing education, it is paramount to address the ethical implications of their implementation. This article explores the realm of cyberethics (a field of applied ethics that focuses on the ethical, legal, and social implications of cybertechnology), highlighting the ethical principles of autonomy, nonmaleficence, beneficence, justice, and explicability as a roadmap for facilitating AI integration into nursing education. Research findings suggest that ethical dilemmas that challenge these five principles can emerge within the context of nursing education; however, adherence to these very principles, which is essential to improving patient care, can offer solutions to these dilemmas. To ensure the ethical and responsible use of Gen AI in nursing education, these principles must be woven into the fabric of curricula, and appropriate guidelines must be developed. Nurse educators have a pivotal role in strategizing comprehensive approaches for ethical AI integration, establishing clear guidelines, and instilling critical thinking among students. Fostering lifelong learning and adaptability is key to ensuring that future nurses can successfully navigate the constantly evolving landscape of health care technology. Future research should investigate the long-term impacts of AI utilization on learning outcomes and ethical decision-making.
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Unchecked AI use can encourage passive learning and cognitive offloading, particularly in environments where rapid completion of tasks is rewarded more strongly than reflective practice.
“This finding reinforces concerns that unchecked AI use can encourage passive learning and cognitive offloading, particularly in environments where rapid completion of tasks is rewarded more strongly than reflective practice [15, 16, 37].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [16]
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- Authors
- De Gagne, J. C., Hwang, H., & Jung, D.
- Affiliations
- Duke University School of Nursing
- Ewha Womans University (KR)
- Corresponding author
- Hwang, Hyeyoung
- Venue
- Nursing Ethics
- Publisher
- SAGE Publications
- Published
- 2023-10-06
- Volume
- 31
- Issue
- 6
- Pages
- 1021-1030
- ISSN
- 0969-7330 (ISSN-L)
- 1477-0989
- OpenAlex
- W4387420891
- Cited by
- 93
- References
- 6
- Citations by year
- 2026: 35 · 2025: 48 · 2024: 7 · 2023: 3
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nursing Ethics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- cyber ethics, nursing education, generative AI, AI ethics, ethical principles, autonomy, non-maleficence, beneficence, justice, explainability, ethical dilemmas, nursing faculty, critical thinking, lifelong learning, ethical decision-making
- MeSH terms
- Artificial Intelligence, Curriculum, Education, Nursing, Ethics, Nursing, Humans
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