This conceptual paper proposes a framework for redesigning asynchronous assessments in online graduate nursing education in response to generative artificial intelligence, and proposes four strategies grounded in the community of inquiry framework, authentic assessment theory, experiential learning theory, and transformative learning theory: experience-anchored analysis, multimodal documentation portfolios, asynchronous verbal exchange, and asynchronous collaborative process.
Abstract
This conceptual paper proposes a framework for redesigning asynchronous assessments in online graduate nursing education in response to generative artificial intelligence (AI). Traditional text-based assignments are vulnerable to AI-generated submissions, and conventional plagiarism detection software performs poorly against such content. We argue for redesigning assessments, rather than increasing surveillance. We propose four strategies grounded in the community of inquiry framework, authentic assessment theory, experiential learning theory, and transformative learning theory: experience-anchored analysis, multimodal documentation portfolios, asynchronous verbal exchange, and asynchronous collaborative process. None of the strategies are new, and each has a long history in nursing and health professions education. The contribution of this work is its integration of the four strategies into a single design rationale that would be responsive to the pressure placed by AI on asynchronous assessments. We use ‘AI-resistant’ in this paper in a bounded sense, that is, for designs that can reduce the feasibility of the substitution of students’ work with AI, rather than designs that can prevent any form of AI involvement.
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
This commentary is deliberately framed as a focused extension of the original study’s individual-level findings to the institutional level, rather than a stand-alone organizational literature review, and its scope has been kept correspondingly concise.
Introduction: Generative artificial intelligence (AI) can produce realistic clinical scenarios on demand and deliver immediate, individualized feedback, yet its use to teach ethical reasoning, rather than to address the ethics of AI itself, remains underexplored in interprofessional healthcare education. Aim: This pilot study examined how interprofessional healthcare students perceived an AI-enhanced, case-based platform designed to support ethical decision-making across physical therapy, occupational therapy, speech-language pathology, and audiology. Methods: Students enrolled in an interprofessional education course completed an online module of 20 instructor-vetted, AI-generated ethics cases and an optional post-activity survey of Likert-scale and open-ended items. Quantitative data were analyzed descriptively and qualitative responses were analyzed through content analysis. Results: Ten students responded. Within this small sample, perceptions of platform utility and usability were strongly positive, with all respondents agreeing that immediate feedback and scenario variety supported learning. Perceptions were more divided when the platform was compared directly with traditional classroom learning, and respondents identified pacing and auto-scrolling as usability concerns. Conclusions: These preliminary findings suggest AI-enhanced case-based platforms can engage students and support applied ethics learning but are best positioned to complement rather than replace traditional instruction. Findings are exploratory given the small, demographically limited sample.
L. Rankine, J. Van Bussel, S. Moodie et al.· medRxiv· 0 citations
BACKGROUND
Integrating generative artificial intelligence (GenAI) into course learning has emerged as a significant trend in the future development of nursing education. Students are central to learning activities, and their authentic experiences with GenAI-assisted learning are essential for understanding how educational innovations are perceived and implemented. However, there remains a paucity of qualitative studies focusing on students with GenAI-assisted learning experiences in the classroom environment. This study therefore aimed to explore nursing undergraduates' experiences, perceptions, and opinions following their participation in structured GenAI-assisted learning in a formal classroom environment involving both instructors and peers.
METHODS
Based on the guidance of the Unified Theory of Acceptance and Use of Technology (UTAUT), a semi-structured interview outline was designed. Using purposive sampling, 12 nursing undergraduates with learning experiences assisted by GenAI in the classroom environment were recruited for semi-structured interviews, and the data were analyzed using the six-phase reflexive thematic analysis method.
RESULTS
Analysis yielded four themes: (1) Perceived usefulness and ease of use, (2) Social facilitation and resource support, (3) Risk concerns, and (4) Expectations and suggestions.
CONCLUSION
Structured GenAI-assisted learning was perceived to support nursing undergraduates' classroom learning and skill development. Future efforts may focus on optimizing teaching strategies, strengthening ethical guidance, and developing nursing-specific GenAI tools to promote the responsible and effective integration of GenAI into nursing education.
Dan Song, Yue Zhang, Xia Xu et al.· BMC Nursing· 0 citations
Generative AI tools can produce polished academic text on demand, undermining the validity of assessments that treat written submissions as evidence of individual learning. Detection-based countermeasures have demonstrated variable accuracy and equity concerns. This paper does not report empirical outcomes or validation data. It proposes a framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning. The framework comprises four pillars: (1) process-based documentation; (2) oral defense integration; (3) authentic task design; and (4) transparent AI-use policies aligned with intended learning outcomes. These are operationalized through a rubric model, an oral-defense protocol, and an assessment vulnerability audit tool. Illustrated primarily through health sciences education, with wider relevance to professional and practice-oriented disciplines, the framework treats generative AI as a catalyst for re-examining how assessment evidence is generated rather than solely as an integrity threat. All proposed tools are design instruments for future empirical evaluation, not psychometrically validated instruments.
Dragan Nikolić, M. Basta Nikolić· Frontiers in Artificial Inte...· 0 citations
Artificial intelligence (AI) is quickly revolutionizing higher education; however, there is high heterogeneity regarding the adoption of AI amongst the students. In the context of nursing education, where successful learning, critical thinking skills, and professional accountability are crucial, it becomes essential to understand how the students interact with the AI applications. The current study was conducted to determine the AI-use profiles among nursing students and how they relate to academic outcomes and demographics.
We used a cross-sectional survey among nursing students. AI use for learning purposes was assessed using four binary indicators capturing use for understanding concepts, summarizing content, drafting assignments, and language support. Study engagement was measured using selected items from the Online Student Engagement framework and collapsed into three ordinal categories. We assessed academic performance using three self-reported items. We performed a latent class analysis to identify distinct student profiles based on AI use and engagement indicators. Multinomial logistic regression examined associations between demographic factors and class membership. Multiple linear regression assessed differences in academic performance across classes.
The best model was a three-profile solution with a good classification accuracy. The profiles included strategic engagers, moderate users, and passive or low engagers. As expected, strategic engagers were characterized by high levels of engagement with all kinds of behaviors studied, whereas Passive used consistently reported low levels of engagement. Usage indicators of AI revealed little variation among classes. The performance scores of strategic engagers were significantly better compared with those of passive users (β = 2.39, 95% CI = 2.08–2.69). No significant association was found for demographic characteristics.
Nursing students can be categorized into distinct profiles based on their patterns of AI use and study engagement. Our findings showed that study engagement, but not AI use alone, was the key factor associated with academic performance. These findings highlight the importance of promoting effective learning strategies alongside AI integration. Educational interventions should focus on guiding students toward strategic and responsible use of AI to enhance learning outcomes.
Majed M. Aljabri, Bandar S. Alharbi, Endale Alemayehu Ali· Frontiers in Medicine· 0 citations