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Artificial intelligence and academic integrity in nursing education: A mixed methods study on usage, perceptions, and institutional implications
Journal article   Open access   Peer reviewed

Artificial intelligence and academic integrity in nursing education: A mixed methods study on usage, perceptions, and institutional implications

Maggie Zgambo, Martina Costello, Melanie Buhlmann, Justine Maldon, Edah Anyango and Esther Adama
Nurse education today, Vol.153, 106796
2025
DOI: https://doi.org/10.1016/j.nedt.2025.106796
PMID: 40517666
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Published692.82 kBDownloadView
CC BY V4.0,  Open Access

Abstract

Academic integrity Academic misconduct Artificial intelligence Implications Intention to adopt Mixed methods Nursing education Nursing students
Background The rise of artificial intelligence (AI) use in higher education has generated substantial debate among academics and students, given the potential for students to engage in academic misconduct through the misuse of AI. Academics argue that AI poses a serious threat to the foundational development of nurses through the questionable integrity of AI-generated academic work and by undermining the development of critical thinking skills essential for professional practice. However, there is limited research on nursing students' integration of AI technologies in their studies. Method This study utilised a convergent parallel mixed methods approach to develop a multiphase approach with convergent parallel techniques for the qualitative and quantitative phases. The quantitative method utilised a Qualtrics-powered online survey to engage 188 nursing students, exploring various domains related to AI use. In the qualitative phase, in-depth interviews with 13 purposively sampled students provided deeper insights. The qualitative data were analysed using an inductive thematic analysis approach, while the quantitative data were analysed using SPSS. Result In the survey, 24 % of respondents reported using AI, ranging from moderate to extensive usage. In logistics regression analysis, hearing about AI (OR = 3.9; CI 1.07–10.2; p < 0.05), the belief that AI was useful in the studies (OR = 5.5; CI 1.7–17.3; p < 0.01), and the perception that learning to use AI is easy (OR = 3.4; CI 1.1–11.1; p < 0.05) predicted AI use. Qualitative findings revealed that all students used AI for various academic purposes. The ‘fascinating’, ‘intelligent’ and ‘efficient’ nature of AI in handling ‘time-consuming’ academic tasks motivated its use. However, concerns about breaching academic integrity and the value of achieving success through personal effort served as deterrents. Conclusion The findings suggest that while AI's efficiency drives students to adopt it, they remain cautious about its ethical implications, leading to uncertainty in its application within academic practices. This highlights the critical need for institutional support and explicit guidelines on responsible AI integration in educational settings.

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Collaboration types
Domestic collaboration
Citation topics
6 Social Sciences
6.185 Communication
6.185.2797 AI Ethics
Web Of Science research areas
Education, Scientific Disciplines
Nursing
ESI research areas
Clinical Medicine
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