Faculty speakers recommended that course-level decisions remain with instructors, while the system provide equitable access, training, FERPA-compliant data protections and enforceable processes for AI-related academic integrity issues.

ISM University of Management and Economics has found plagiarism and improper use of text in Prime Minister Mindaugas Sinkevičius's doctoral dissertation, but says the violations do not warrant revoking his degree. The university's ethics committee has given him six months to correct them, LRT rep…

The Institute of Electrical and Electronics Engineers (IEEE) has retracted 1,417 conference papers i...

The University of Maine System Academic and Student Affairs Committee heard student and faculty views on artificial intelligence and discussed whether the board should develop system-level guidance while preserving faculty academic freedom.

Student representatives told trustees that opinions on AI vary widely: some students welcome it as a tool, others are organizing petitions to limit classroom use and express concerns about privacy and academic integrity.

Background: The rapid integration of generative AI (GenAI) into #nursing education has prompted widespread concern about academic integrity and assessment validity. However, this dominant discourse may obscure a more fundamental challenge: preparing #nursing students for clinical practice environments where AI already mediates triage, diagnostics, documentation, and clinical decision-making. Objective: This integrative review examines how GenAI is conceptualized within the #nursing education literature and investigates the extent to which current discourse addresses professional formation rather than academic compliance. Methods: Following Whittemore and Knafl’s integrative review methodology, a systematic search of PubMed, Google Scholar, and SciSpace (Typeset) was conducted for literature published between January 2022 and February 2026. Two reviewers independently screened 456 records, with 119 papers meeting the inclusion criteria. The methodological quality of empirical studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Corpus-level descriptive mapping quantified dominant themes and conceptual patterns, while an interpretive synthesis examined how GenAI was framed and the extent to which professional formation elements, including identity, moral agency, and clinical judgment, were addressed. Results: Of the 119 unique studies analyzed, 84% (n=100) predominantly framed GenAI as a content-generation tool and academic integrity threat. Only 35.3% (n=42) conceptualized AI as a pedagogical tool, and a critical gap was identified in professional formation: identity formation was addressed in only 10% (n=12) of papers and moral agency in just 5% (n=6). Explicit acknowledgment that AI already routinely mediates frontline clinical decisions (eg, sepsis alerts and fall risk algorithms) in current practice settings was notably absent. The discourse emphasizes governance and faculty literacy, but sustained curricula addressing #nurse identity in AI-mediated care remain underdeveloped. Conclusions: Current #nursing education discourse predominantly frames AI as a writing tool and an academic integrity concern, failing to prepare students for the algorithmic practice environments they will inhabit. Nursing education must reframe AI as a professional formation issue, explicitly addressing how algorithms reshape clinical judgment, moral agency, and professional identity.

Toggle navigation Student Community Standards Good-Human-Campaign Academic Integrity Your Rights & Responsibilities Appeals Getting Support Resources + Contact Us This year, MRU Academic Integrity Days will take place on October 22nd + 23rd Academic Int...

Publisher retracts 253 AI-assisted papers by UChicago statistician amid quality concerns.