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

Authors

Maggie Zgambo, Edith Cowan UniversityFollow
Martina Costello, Edith Cowan UniversityFollow
Melanie Buhlmann, Edith Cowan UniversityFollow
Justine Maldon, Edith Cowan UniversityFollow
Edah Anyango, Edith Cowan UniversityFollow
Esther Adama, Edith Cowan UniversityFollow

Author Identifier

Maggie Zgambo: https://orcid.org/0000-0001-8667-795X

Martina Costello: https://orcid.org/0000-0003-2280-3368

Melanie Buhlmann: https://orcid.org/0000-0002-3528-3165

Justine Maldon: https://orcid.org/0000-0001-7527-6042

Edah Anyango: https://orcid.org/0000-0002-8677-5047

Esther Adama: https://orcid.org/0000-0002-7771-2722

Document Type

Journal Article

Publication Title

Nurse Education Today

Volume

153

Publisher

Elsevier

School

School of Nursing and Midwifery / Centre for Learning and Teaching

RAS ID

82166

Funders

Edith Cowan University School of Nursing and Midwifery Executive Dean's Award – 2023 Teaching and Learning Grant

Comments

Zgambo, M., Costello, M., Buhlmann, M., Maldon, J., Anyango, E., & Adama, E. (2025). Artificial intelligence and academic integrity in nursing education: A mixed methods study on usage, perceptions, and institutional implications. Nurse Education Today, 153, 106796. https://doi.org/10.1016/j.nedt.2025.106796

Abstract

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.

DOI

10.1016/j.nedt.2025.106796

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.