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Refining assessment design and practice in nursing and midwifery education in the Era of generative AI: a discussion paper

  • Yenna Salamonson
  • , Pauletta Irwin
  • , Rachel Kornhaber
  • , Debbie Kirk
  • , Shirley McGough
  • , Kat Vakavosaki
  • , Lucie Ramjan
  • , Jacqueline Bloomfield
  • , Annabel Matheson
  • , Melanie Greenwood
  • University of Wollongong
  • Australian Centre for Integration of Oral Health (ACIOH)
  • Charles Sturt University
  • Adelaide University
  • Federation University Australia
  • Curtin University
  • University of Waikato
  • The University of Sydney
  • University of Tasmania

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Number of pages13
JournalJournal of Clinical Nursing
DOIs
Publication statusE-pub ahead of print (In Press) - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 4 - Quality Education
    SDG 4 Quality Education
  3. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

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