Academic Integrity and the Nursing Researcher: A Narrative Review with Case Studies in the AI Era

Introduction: Artificial intelligence (AI) is rapidly transforming nursing research by enhancing the efficiency of data analysis, literature reviews, and manuscript preparation. In Malaysia, nursing researchers are increasingly using AI tools to meet their academic demands. Despite the growing global discourse on AI ethics, research on the emergence of dilemmas related to academic integrity within Malaysian nursing education is limited. Current guidelines provide minimal direction on AI governance, and restorative strategies tailored to nursing contexts, such as mentorship, culturally grounded ethics, and postgraduate supervision, remain underdeveloped in this area. This review examines ethical dilemmas and restorative strategies at the micro, meso, and macro levels, utilising theoretical frameworks and relevant case studies to inform the responsible application of AI in Malaysian nursing research. Materials and methods: A narrative review was conducted using the PubMed, Scopus, and Google Scholar databases. The review focused on literature from 2020 to 2025 and employed Boolean combinations of keywords, including “academic integrity,” “AI in nursing,” “ethical scholarship,” and “Malaysian research ethics.” Deontological ethics guided the thematic synthesis, Goffman’s stigma theory, and Allport’s contact theory. Results: Micro-level dilemmas included citation misuse and overreliance on AI. Mezzo-level challenges involve institutional gaps in AI governance and mentorship programs. Macro-level concerns highlight disparities in AI access and the need for culturally sensitive policy reforms. Case examples illustrate issues such as ghost authorship and omission of consent. Conclusion: Upholding academic integrity in the era of AI demands reflective scholarship, transparent governance, and a renewed commitment to dignity and justice.