Navigating challenges and opportunities: Nursing student’s views on generative AI in higher education
Abstract
Aim This qualitative study aims to explore the perspectives of nursing students regarding the application and integration of generative Artificial Intelligence (AI) tools in their studies.Background With the increasing prevalence of generative AI tools in academic settings, there is a growing interest in their use among students for learning and assessments.Design Employing a qualitative descriptive design, this study used semi-structured interviews with nursing students to capture the nuanced insights of the participants.Methods Semi-structured interviews were digitally recorded and then transcribed verbatim. The research team reviewed all the data independently and then convened to discuss and reach a consensus on the identified themes.Results This study was conducted within the discipline of nursing at a regional Australian university. Thirteen nursing students, from both first and second year of the programme, were interviewed as part of this study. Six distinct themes emerged from the data analysis, including the educational impact of AI tools, equitable learning environment, ethical considerations of AI use, technology integration, safe and practical utility and generational differences.Conclusions This initial exploration sheds light on the diverse perspectives of nursing students concerning the incorporation of generative AI tools in their education. It underscores the potential for both positive contributions and challenges associated with the integration of generative AI in nursing education and practice.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Quality assurance gaps included a lack of verification standards and risks of generating inaccurate content, requiring stringent controls and validation protocols.
“Quality assurance gaps included a lack of verification standards and risks of generating inaccurate content, requiring stringent controls and validation protocols”
From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Summers et al., 2024
Several studies highlight the importance of equitable access to licensed AI platforms, curated digital content, case studies, and lesson plan templates tailored for AI-enhanced teaching.
“Several studies highlight the importance of equitable access to licensed AI platforms, curated digital content, case studies, and lesson plan templates tailored for AI-enhanced teaching (Abduljawad, 2024; Strzelecki & ElArabawy, 2024; Summers et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Summers et al., 2024
Nursing students believed GAI improves understanding and efficiency but worried it may impede critical thinking and emotional skills.
“Two previous descriptive interview studies showed mixed attitudes toward GAI; medical students and educators found it enhances clinical reasoning [22], while nursing students believed it improves understanding and efficiency but worried it may impede critical thinking and emotional skills [23].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [23]
Excessive use of GAI could hinder critical thinking and decision-making, risking dependency on GAI for knowledge acquisition.
“Excessive use of GAI could hinder critical thinking and decision-making, risking dependency on GAI for knowledge acquisition, which is findings in medical students [8] and nurse students [9, 23].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [23]
Ensuring access to GAI tools is crucial for fairness and addressing ethical concerns related to academic integrity.
“Ensuring access to GAI tools is crucial for fairness and addressing ethical concerns related to academic integrity [23].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [23]
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Tong, A., Sainsbury, P., & Craig, J. (2007) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzm042 · Cited by 42,509 · see citing works
Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education
Kolb, A. Y., & Kolb, D. A. (2005) · Academy of Management Learning & Education · doi:10.5465/amle.2005.17268566 · Cited by 4,980 · see citing works
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504 · see citing works
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · see citing works
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261 · see citing works
Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023) · Education Sciences · doi:10.3390/educsci13090856 · Cited by 877 · see citing works
Authentic assessment: creating a blueprint for course design
Villarroel, V., Bloxham, S., Bruna, D., Bruna, C., & Herrera-Seda, C. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1412396 · Cited by 560 · see citing works
Tam, W., Huynh, T., Tang, A., Luong, S., Khatri, Y., & Zhou, W. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105917 · Cited by 221 · see citing works
Shirazi, F., & Heidari, S. (2019) · Journal of Nursing Research · doi:10.1097/jnr.0000000000000307 · Cited by 179 · see citing works
Irwin, P., Jones, D., & Fealy, S. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105835 · Cited by 92 · see citing works
Sharma, M., & Sharma, S. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105972 · Cited by 76 · see citing works
Emotional Intelligence and Critical Thinking in Nursing Students
Christianson, K. L. (2020) · Nurse Educator · doi:10.1097/nne.0000000000000801 · Cited by 68 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
El Arab, R. A., Alshakihs, A. H., Alabdulwahab, S. H., Almubarak, Y. S., Alkhalifah, S. S., Abdrbo, A., Hassanein, S., & Sagbakken, M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1666005 · Cited by 113 · see citing works
The Contribution of Artificial Intelligence in Nursing Education: A Scoping Review of the Literature
Cucci, F., Marasciulo, D., Romani, M., Soldano, G., Cascio, D., De Nunzio, G., Caldararo, C., Rubbi, I., Vitale, E., Lupo, R., et al. (2025) · Nursing Reports · doi:10.3390/nursrep15080283 · Cited by 60 · see citing works
Lindbäck, Y., Schröder, K., Engström, T., Valeskog, K., & Sonesson, S. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07106-w · Cited by 22 · see citing works
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
Amankwaa, I., Ekpor, E., Cudjoe, D., Kobiah, E., Fuseini, A.-K. J., Diebieri, M., Gyamfi, S., & Brownie, S. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106822 · Cited by 20 · see citing works
- Authors
- Summers, A., Haddad, M. E., Prichard, R., Clarke, K.-A., Lee, J., & Oprescu, F.
- Affiliations
- University of the Sunshine Coast (AU)
- Corresponding author
- Summers, Anthony
- Venue
- Nurse Education in Practice
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 79
- Pages
- 104062
- ISSN
- 1471-5953 (ISSN-L)
- OpenAlex
- W4400520061
- Cited by
- 67
- References
- 14
- Citations by year
- 2026: 37 · 2025: 28 · 2024: 2
- FWCI
- 6.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education in Practice (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- generative AI, nursing students, nursing education, student perceptions, artificial intelligence in higher education, equitable learning environments, ethics of AI use, technology integration, generational differences, assessment
- MeSH terms
- Adult, Artificial Intelligence, Australia, Education, Nursing, Baccalaureate, Female, Humans, Interviews as Topic, Male, Students, Nursing, Qualitative Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
