Artificial Intelligence Use, Perceptions, and Perceived Impact Among Nursing Students in Albania: Cross-Sectional Study
ABSTRACT
Background:
Artificial intelligence is being increasingly integrated into education and healthcare, offering innovative opportunities to improve the learning process, the understanding of clinical cases, and the self-confidence of students. However, it remains necessary to assess nursing students’ perceptions of AI and its impact on their academic and professional development.
Objective:
The aim of the study was to assess the use of AI, students’ perceptions of it, and its impact on academic performance and professional preparation among nursing students.
Methods:
A descriptive cross-sectional study was conducted at the Faculty of Medical Sciences in Elbasan. Data were collected through a structured online questionnaire assessing demographic characteristics, AI use, perceptions, and its impact. The data were analyzed using SPSS 23.0, including descriptive statistics, the chi-square test, and crude odds ratios (COR).
Results:
A total of 279 students participated in the study. Most of them (83.9%) reported using AI, mainly for information searching (92.3%) and understanding lectures (41%). Virtual assistants such as ChatGPT were the most commonly used tools (78.2%). Academic average was a factor significantly associated with AI use, whereas place of residence and cycle of study did not show significant associations. Students reported a moderate impact on understanding lectures and exam preparation, but a limited impact on practical skills, critical thinking, and empathy.
Conclusions:
AI represents an effective supportive tool in nursing education by improving theoretical understanding and academic efficiency. However, its impact on human competencies remains limited. In order to optimize its benefits and prevent dependency, a structured and ethical integration is needed.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.