The impact of ChatGPT on nursing education: a qualitative study based on the experiences of faculty members
Abstract
Background Recent advancements in artificial intelligence technologies particularly ChatGPT have supported learning and fostered critical thinking in nursing education. However, integrating these tools into academic settings requires both ethical and strategic planning.Aim This study aims to explore the perspectives of nursing faculty members regarding the integration of ChatGPT into nursing education.Methods This descriptive phenomenological study was conducted with 14 nursing faculty members. Data were collected using a socio-demographic information form and a semi-structured interview guide and analyzed using thematic analysis methods. The COREQ checklist was followed.Results The average age of the nursing educators was 38.46 ± 5.23 years, and their average professional experience was 14.21 ± 3.35 years. Six main themes and twelve sub-themes were identified from the interviews. The main themes included information accuracy and reliability, contributions to educational processes, ease of use and challenges, development of professional knowledge and skills, integration into nursing practices, and the long-term impacts on nursing education.Conclusion ChatGPT has the potential to enhance nursing education by supporting theoretical learning, improving efficiency, and fostering personalized learning experiences. To successfully integrate ChatGPT into nursing curricula, educational institutions should implement specific strategies, such as faculty training programs and AI literacy initiatives, to equip educators and students with the necessary skills to use AI tools effectively.Implications for nursing and health policy Educational institutions should develop clear policies for the ethical and strategic integration of AI tools like ChatGPT. Future research should focus on specific methodologies, such as longitudinal studies that assess the impact of ChatGPT on student learning outcomes, as well as its long-term effects on nursing practice and healthcare services.
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Analyzing the interaction process between students and AI chatbots can assist nursing educators in identifying key intervention points within the learning process, supporting the creation of more effective learning experiences, helping students understand their learning patterns and habits, and thus promoting the enhancement of self-regulated learning skills.
“For instance, analyzing the interaction process between students and AI chatbots can assist nursing educators in identifying key intervention points within the learning process, supporting the creation of more effective learning experiences, helping students understand their learning patterns and habits, and thus promoting the enhancement of self-regulated learning skills (62, 66).”
From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [62]
Sarıkahya et al. (62) highlighted that while ChatGPT enhances teaching efficiency and flexibility, concerns remain regarding students' overreliance and reduced critical engagement.
“Sarıkahya et al. (62) highlighted that while ChatGPT enhances teaching efficiency and flexibility, concerns remain regarding students’ overreliance and reduced critical engagement.”
From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [62]
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- Authors
- Durmuş Sarıkahya, S., Özbay, Ö., Torpuş, K., Usta, G., & Çınar Özbay, S.
- Affiliations
- Artvin Coruh University (TR)
- Trabzon University (TR)
- Venue
- Nurse Education Today
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 152
- Pages
- 106755
- ISSN
- 0260-6917 (ISSN-L)
- OpenAlex
- W4409509954
- Cited by
- 60
- References
- 54
- Citations by year
- 2026: 54 · 2025: 6
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Update notices
- Open access
- closed
- Licence
- Locations
- Nurse Education Today (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
- ChatGPT, artificial intelligence in education, nursing education, nursing faculty perspectives, AI literacy, faculty training, AI ethics in education, personalized learning
- MeSH terms
- Generative Artificial Intelligence, Adult, Artificial Intelligence, Curriculum, Education, Nursing, Faculty, Nursing, Female, Humans, Learning, Male, Middle Aged, 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 ›
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