'Co-pilot won't teach you to be a nurse': nursing academics' perspectives on GenAI use in undergraduate education

Synthesis: Dabkowski and colleagues interviewed 22 nursing academics from universities across Australia and New Zealand in February 2025 and analysed the transcripts with reflexive thematic analysis, asking how they perceive and experience GenAI in undergraduate nursing education. Three themes emerged with nine minor themes beneath them: Navigating the Unknown: Ambiguity in GenAI Use, GenAI Challenging Nursing's Core Values, and Developing Ethical Nurses in a Digital Age. Participants reported inconsistent policy, divergent collegial attitudes, a drift back to invigilated and oral assessment, and fear that AI-mediated coursework lets students pass without the reasoning that nursing practice depends on. The authors argue GenAI troubles more than Academic Integrity procedure: it disturbs the basis on which educators judge whether a student is safe to practise.

Key Findings

1. Policy was absent, late, or not written for the people implementing it. Participants described "no clear, timely and practical guidance," with one saying "There was no proper information regarding AI use in our university... I think that's the first time something proper has come out" [P3]. Another questioned the provenance of the rules: "Sometimes I think the people who are in charge of policies are not on the ground dealing with the outcome of the policy" [P18], and a third that "the IT is probably evolving quicker than we can keep up with at the moment... I just don't think we're there at the moment" [P15]. Inconsistent practice followed: "Some academics absolutely ignore the use of AI or Grammarly in their course. But then the next one is absolutely stringent, so there needs to be some consistency" [P19].
2. Academic attitudes diverged sharply, from scepticism to acceptance. Some rejected the technology's creative value: "I think artificial intelligence is a bit of a misnomer. It's not original thought. It's just a reflection of what already exists" [P11]. Others saw resistance as futile: "We need to accept that it's here and we need to accept that students are going to use it. You can't put the genie back into the bottle" [P16], and "Maybe we need to stop fighting it and look at ways to use it productively to help our students" [P2]. One participant described tools as "an enthusiastic but somewhat unreliable assistant" [P11]; another worried about colleagues who tell students "You must not use it – if you use it, you will fail" [P21].
3. The line participants drew was replacement, not use. Editing and writing support were tolerated; generation of whole submissions was not. "It's purely when it replaces the student learning that I have an issue... they shouldn't be allowed to use it to replace learning" [P14]. Another framed the aim as critical engagement: "It's about how we can use it authentically, how we can use it to enable us to think critically, not take information for granted" [P1].
4. Assessment design shifted toward invigilation, orals, and process evidence. "We can't rely on a 2000 word essay anymore" [P4]; "eventually we are going to have to go back to written exams or... one-on-one so that people have to actually tell their knowledge" [P22]. Practical measures appeared alongside: rubrics "written in a way that you can identify AI" [P20], "five marks for showing us a screenshot of their literature search terms from a database" [P4], and oral vivas with several scenarios because "you can actually look at their clinical reasoning. It's that student at that time, that's their work" [P19]. Counter-evidence came too: "If students choose to use AI all the way through for their assessments and they get to the exam and know nothing, it's like 'what did you think was gonna happen?'" [P14].
5. Core values, not just marks, were seen as at stake. Participants positioned themselves as "the guardians of the profession" [P9] and as "the voice of the patient," arguing "If we can't do that and have nursing students that are honest and caring, then we're not doing our job" [P9]. One weighed reputation against care directly: "I would rather the students who learn from underneath me go out there and be phenomenal nurses who have no idea about AI than have a nurse go out there who's like, 'I'm an expert in AI' but have no idea how to care for a person" [P14]. Another tied the qualification to safety: "It needs to be legitimate because we need to consider patients' safety first and foremost" [P15].
6. Misconduct was read as a rehearsal for unsafe practice. "If a student's willing to breach AI then what shortcuts will they take in the clinical area?" [P2]; "those that get away with academic misconduct at the university will have issues with professionalism when they get onto the real world" [P9]. One participant linked dishonesty to documentation: "I have often seen nurses manipulate those results to not result in a clinical review" [P6], and another quoted students who had used AI undetected [P1]. On hallucinated content one was unequivocal: "If I think about the kind of nurse that we need in a healthcare setting, it's certainly not someone that sources fake references for information, because that's quite ethically and morally wrong" [P17].

How the study was conducted

The paper reports one analytic strand of a broader qualitative study of academic integrity in undergraduate nursing education. A qualitative descriptive design was implemented through semi-structured interviews conducted over Microsoft Teams, reported against the COREQ checklist, with reflexive thematic analysis following Braun and Clarke (2021). Three researchers coded inductively, then grouped codes into themes that the team reviewed and refined iteratively; the authors ran a side-by-side analysis of the academic integrity and GenAI strands rather than one combined account.

Recruitment was purposive, distributed through the Australasian Academic Integrity Network mailing list, a network of more than 1100 staff at approximately 140 institutions in Australia and New Zealand. Eligibility required current teaching or coordination in an undergraduate nursing programme; permanent and sessional staff were both eligible. Twenty-two academics responded and all were interviewed, between 4 and 28 February 2025. The average interview was 39.05 minutes (range 19.53 to 59.45). The majority were female with five identifying as male; average academic experience was 6.4 years (range 1 to 20); nine held doctoral qualifications and several held academic integrity leadership roles. Data collection stopped once the team judged information power sufficient, having pre-estimated that 15 to 20 interviews would likely suffice (Malterud et al. 2016). Participants were offered the chance to check their transcripts, and all data were de-identified at transcription with numeric codes. All five researchers held Academic Integrity Officer roles at the time of analysis, four of them nursing academics, and the paper names this positionality as a possible influence on interpretation.

Knowledge translation and clinical reasoning at risk

The second theme moves furthest from integrity policing. The objection here is about knowledge translation rather than authorship: what happens to reasoning that is never practised. "If using AI in assessment tasks to generate answers for caring for a deteriorating patient, how will the student develop skills?" [P13], and more bluntly, "The biggest issue that I find is that it's inhibiting their critical thinking" [P17]. The worry was explicitly longitudinal: "in three or five years' time, we're going to probably have a big cohort of students that are just not going to be fit for practice" [P9], reinforced by concern that "students potentially not thinking that there's base knowledge that they need to know, especially around patient assessment" [P15].

Under the third theme, participants argued for neither prohibition nor permissive access. They called for deliberate teaching of ethical, transparent use, staff development, and unit redesign, with one describing "lots of professional development around writing new units to incorporate GenAI" [P10], and rejecting avoidance because "it would be unhelpful to actually not allow our nursing students to learn how to use it efficiently, ethically, effectively, when others will" [P21]. Several described using GenAI's fallibility as a teaching device: AI-generated bots role-playing a graduate coordinator for interview practice, or deliberately inaccurate outputs posted for discussion so students "understand that not everything we get from it is accurate" [P21]. The paper's closing quote gives its title: "Copilot won't teach you to be a nurse" [P11].

Implications for policy and practice

The authors call for nursing-specific guidance aligned with the profession's ethical codes and standards, rather than generic institutional AI policy. They argue GenAI literacy and ethical reasoning should be embedded across the curriculum (Simms, 2025), that assessment design needs continuing review so tasks still evaluate a student's own reasoning and judgement, with more weight on authentic and process-oriented formats, and that staff development and co-design with staff, students and Stakeholders are needed for any of this to hold. They name the TWO PRISMS assessment design framework as one way to work through this (Winchester 2025). The paper endorses Dawson et al. (2024): validity matters more than cheating.

Two caveats belong with the findings. Recruitment through one professional network means the views of nursing academics outside it, and outside Australia and New Zealand, are not represented, and the accounts are self-reported on a sensitive topic where candour may have been constrained. The authors also note that the team's own roles as nursing educators and Academic Integrity Officers shaped what was asked and how it was read, and that GenAI tools and institutional responses are changing fast enough that the findings describe a particular moment. The paper concludes that GenAI in nursing education cannot be treated as a generic higher education issue, because it changes how educators judge competence, accountability and readiness for practice.

Connected Concepts

• Nursing Education — the discipline whose assessment and accreditation logic the paper interrogates
• Academic Integrity — reframed from a compliance process into a question about professional formation
• Generative AI — the technology whose use by nursing students participants were making sense of
• Critical Thinking — the capability participants most feared overreliance would blunt
• Assessment — the mechanism through which readiness for practice is inferred, and the site of redesign
• Authentic Assessment — the direction of travel participants described, including process evidence and orals
• Assessment Validity — the paper's underlying position, following Dawson et al., that validity outranks cheating
• Educational AI Policy — the missing or late institutional guidance participants described
• Educational Development — the staff development the participants asked for, and the unit-redesign work it implies
• Teaching — academics as guardians of professional values and as the subject of staff development
• Transfer of Learning — the knowledge-translation worry: theory that never becomes clinical skill
• Teacher AI Competency — the confidence and consistency gap participants attributed to colleagues
• Simulation — AI role-play and scenario work used deliberately to build reasoning
• Curriculum Design — where the authors locate GenAI literacy and values-based integration
• Career Development and Readiness — the paper's real stake: whether graduates are fit for practice

Connected Articles

• When the algorithm enters the classroom: A critical integrative review of large language models, nursing education — Nursing professional identity and LLM use, an adjacent reading of the same discipline
• Nursing Students' and Faculty Experiences with Artificial Intelligence in Education: A Qualitative Study Using the Technology Acceptance Model — Nursing students' AI experiences read through the technology acceptance model
• Transforming Nursing Education with Artificial Intelligence: A Systematic Review (2010–2025) — Broader account of AI transforming nursing curricula
• AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review — Simulation-based AI in nursing education, the constructive use participants described
• The Integrity of Psychology Assessments in the AI Age: A Critical Examination — Programme-level evidence that the pass boundary, not detection, is the operative variable
• The Impact of Generative AI on Academic Integrity of Authentic Assessments Within a Higher Education Context — Why authentic assessment alone does not safeguard integrity
• From AI Use to Critical Thinking Among Medical Students: A Moderated Mediation Perspective on Cognitive Load and Self-Regulated Learning — Critical thinking and AI use in the neighbouring health professions
• Purpose Before Policy: Academic Integrity, Generative AI, and Rhetorical Stance — Purpose before policy in academic integrity responses to AI

Citation

Dabkowski, E., Missen, K., Allen, L., Whitehead, D., & Worn, R. (2026). 'Co-pilot won't teach you to be a nurse': Nursing academics' perspectives on GenAI use in undergraduate education. International Journal for Educational Integrity, 22(11).