Eroding scholarly integrity: Confronting the misuse of generative AI in nursing education.
TL;DR
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.
