AIMS
This paper equips nursing and midwifery academics with strategies to assess student competence in an educational landscape shaped by generative artificial intelligence (GenAI). It examines assessment design approaches, highlighting the shift from tasks reliant on unenforceable rules (discursive changes) toward redesigning assessment mechanics (structural changes) to preserve validity.
BACKGROUND
The rapid adoption of GenAI tools like ChatGPT is transforming higher education, challenging assumptions about teaching, learning, and academic integrity. Traditional assessments are increasingly unsuited when AI can replicate student outputs. Rather than policing AI use, institutions must leverage its potential whilst ensuring assessment remains authentic, equitable, and valid.
METHODS
A narrative review was undertaken, drawing upon peer-reviewed literature, expert commentary, and policy documents related to GenAI in nursing and midwifery education, emphasising assessment design and academic integrity.
DISCUSSION
Two primary approaches are presented. Lane One creates GenAI-resistant tasks fostering higher-order thinking. Lane Two embraces human-AI collaboration, focusing on transparency, process, and developing evaluative judgement. A hybrid Lane Three allows conditional AI use within defined boundaries. Effective redesign requires structural, not merely discursive change, adopting systemic program-level assessment and clarifying acceptable AI use. Supporting staff and students through uncertainty is essential for sustainable reform.
CONCLUSION
Valid and ethical assessment in the GenAI era demands explicit institutional policies, clear communication, and rubrics promoting authentic learning. Embedding structural changes within assessment design, rather than relying on rule enforcement, will ensure nursing and midwifery graduates are prepared to thrive in an AI-enabled world.
IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE
Ensuring patient safety principles remain central to assessments whilst demanding that GenAI integration upholds professional standards will prepare nursing and midwifery graduates to thrive in an AI-enabled healthcare environment.
Y. Salamonson, Pauletta Irwin, R. Kornhaber et al.· Journal of Clinical Nursing· 0 citations
AIM
This study explores nursing educators' perspectives on the challenges and benefits of integrating generative artificial intelligence (AI) into nursing education. There is little empirical evidence on how educators perceive these technologies and how such perceptions influence their integration into curriculum design, teaching practices, assessment, and student research and learning.
DESIGN
Exploratory-descriptive qualitative study underpinned by the Actor-Network Theory.
METHODS
Four focus group sessions were conducted with ten nursing educators across Australia, New Zealand, Austria and Hong Kong. Data were collected via Zoom, transcribed verbatim, and analysed thematically using Braun and Clarke's six-step reflexive framework.
RESULTS
The nurse educators included five women and five men. Ages ranged between 25 and 64 years and a mean 8.75 [SD ±6.36] years of experience as an educator. Three main themes were constructed based on focus group discussions: (1) The AI Dilemma, revealing tensions surrounding academic integrity, policy ambiguity and ethical concerns; (2) The AI Toolkit, identifying pedagogical benefits alongside challenges to critical thinking development; and (3) Educator's AI Odyssey, exposing disparities in institutional preparedness and educator competence. Whilst AI was recognised for enhancing engagement and efficiency, substantive concerns persisted regarding equity, ethical implementation and organisational readiness.
CONCLUSION
Generative AI presents a paradox in nursing education. Whilst it enables innovation and personalised learning, it poses risks to academic integrity and deep learning when implementation lacks ethical consideration and pedagogical rigour.
IMPLICATIONS FOR PROFESSION AND PATIENT CARE
AI-enhanced nursing education must safeguard fundamental nursing values, critical thinking capabilities, ethical reasoning and clinical judgement to ensure the delivery of safe, competent patient care.
IMPACT
Educational and institutional policies must facilitate balanced, ethical and equitable integration of AI in nursing education.
REPORTING METHOD
The Consolidated Criteria for Reporting Qualitative Research (COREQ).
PATIENT OR PUBLIC CONTRIBUTION
No patient or public contribution.
Lucie Ramjan, Belinda McGrath, Clare Walters et al.· Journal of Clinical Nursing· 0 citations