Nursing education must respond proactively by establishing AI literacy frameworks, revising academic integrity policies, and embedding source verification and citation skills into curricula, as generative AI threatens to erode the scholarly standards essential to both academic rigor and professional nursing practice.
Abstract
The integration of generative artificial intelligence (AI) into higher education has introduced powerful tools to support student learning. In nursing education, platforms such as ChatGPT are increasingly used to assist with brainstorming, outlining, and improving academic writing. Although these technologies offer meaningful benefits, their misuse is now creating challenges that threaten the scholarly foundations of nursing education: the misuse of AI to fabricate citations, misattribute authorship, and bypass critical scholarly engagement. The author argues that AI-driven citation fabrication represents a distinct academic integrity challenge that differs from traditional plagiarism in critical ways: it is often unintentional, nearly undetectable by standard tools, and reveals fundamental gaps in students' understanding of evidence-based scholarship. Drawing on a real-world case in undergraduate nursing education, this author examines how a student submitted fabricated journal articles and digital object identifiers (DOIs) alongside misattributed references, mistaking an authored scholarly article for an organizational publication. These errors were not minor oversights; they revealed a lack of understanding of evidence-based scholarship and research ethics. The paper explores how these practices undermine the principles of academic integrity and conflict with the professional standards outlined in the American Association of Colleges of Nursing (AACN) Essentials. This issue reflects not just individual misconduct but a broader pedagogical challenge: preparing nursing students to engage critically and ethically with generative AI technologies. The author argues that nursing education must respond proactively by establishing AI literacy frameworks, revising academic integrity policies, and embedding source verification and citation skills into curricula. Without a clear and enforceable ethical framework, generative AI threatens to erode the scholarly standards essential to both academic rigor and professional nursing practice. This paper contributes to international conversations on AI and academic misconduct in health professions education and calls for a coordinated response to protect the credibility of nursing scholarship and the ethical formation of future nurse leaders.
This paper begins from the author's own practice and observations among colleagues, then examines three sets of questions raised by faculty AI use that have not yet been adequately engaged in the nursing literature.
This conceptual paper proposes a framework for redesigning asynchronous assessments in online graduate nursing education in response to generative artificial intelligence, and proposes four strategies grounded in the community of inquiry framework, authentic assessment theory, experiential learning theory, and transfor...
R. Anders, Kei-Shing Ng, Aderonke Odetayo et al.· Discover Education· 0 citations
This commentary is deliberately framed as a focused extension of the original study’s individual-level findings to the institutional level, rather than a stand-alone organizational literature review, and its scope has been kept correspondingly concise.
This study explored AI-related ethical dilemmas affecting academic integrity at a South African research-intensive university and reframes academic integrity in the AI era as a systemic governance issue requiring clear AI-integrated academic integrity policies, responsible implementation mechanisms, assessment redesign...
Stephen Mongwe, G. van den Berg, Patience Kelebogile Mudau· Journal of Ethics in Higher...· 0 citations
The findings suggest that academic integrity in the age of artificial intelligence (AI) cannot be focused solely on preventing fraud, and this needs to expand to support ethical digital literacy, redesign learning tasks that require human reasoning, and ensure fairness in automated decision-making systems.
W. Phornprasert, W. Nuankaew, Pratya Nuankaew· International Journal of Adv...· 0 citations
Background Artificial intelligence (AI) is quickly revolutionizing higher education; however, there is high heterogeneity regarding the adoption of AI amongst the students. In the context of nursing education, where successful learning, critical thinking skills, and professional accountability are crucial, it becomes e...
Majed M. Aljabri, Bandar S. Alharbi, Endale Alemayehu Ali· Frontiers in Medicine· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.