ABSTRACT
Background
With the continuous advancement of technology, it is predicted that artificial intelligence technologies will be utilised more by academicians and students in nursing education. There are no studies examining the awareness and concerns of artificial intelligence and robot nurses.
Objective
This study was conducted to determine the effect of the training provided to increase nursing students' awareness of artificial intelligence and robot nurses on the anxiety of students towards artificial intelligence.
Design
It is a pre‐test post‐test quasi‐experimental type study in line with STROBE instructions.
Participants
The study was conducted with 282 nursing students between 15 December 2022 and 15 October 2023.
Methods
Introductory Information Form, ‘Artificial Intelligence Knowledge Test (AIK)’, and ‘Artificial Intelligence Anxiety Scale (AIAS)’ were employed to collect the data of the study. The data obtained in the study were analysed through SPSS (Statistical Package for Social Sciences) for Windows 25.0 software.
Results
The nursing students were female (68.8%), and 83.7% of them had heard of concepts of artificial intelligence and robot nurses. The mean knowledge score of the students before the training on artificial intelligence and robot nurses was 63.08 ± 9.96, the mean knowledge score after the training was 66.70 ± 9.85 and a significant difference was detected between the mean knowledge scores (p < 0.05). The mean score of the AIAS scale was 4586 ± 11.18 before the training, 4810 ± 11.59 after the training and a significant difference was detected between the mean scores (p < 0.05).
Conclusion
This study revealed that, as a result of the training provided for artificial intelligence and robot nurses, students' knowledge levels and their anxiety towards artificial intelligence increased. The results may guide the inclusion of artificial intelligence in the curriculum of nursing education for artificial intelligence and robot nurses.
Keywords: artificial intelligence, nurse, robot nurses, student
1. Introduction
The century we are in consists of periods defined as the digital age, in which the development, change and transformation of enterprises are highly intense. Digitalisation has caused major changes and transformations in products and services in all sectors [1, 2]. In this direction, artificial intelligence (AI) is also exposed to digital development, change and transformations at the highest level. AI raises concerns for individuals who will work and take part in business life in the future. Because businesses are rapidly moving towards automation and robotics to ensure a competitive advantage. This will facilitate them to have an advantage in the competitive environment [3].
The term AI was initially introduced by John McCarthy at the Dartmouth Conference in 1956, defining it as “the science and engineering of constructing intelligent machines” [4]. In essence, AI refers to the process of enabling machines to simulate human intelligence, allowing them to think and act in ways similar to humans [5]. In recent years, AI technologies have become increasingly prevalent within global healthcare systems. It is estimated that worldwide expenditure on AI in health technologies will surpass 36 billion US dollars by 2025 [6]. The World Health Organisation (WHO) highlighted in its 2019 Global Strategy on Digital Health report—prepared under the vision of ‘health everywhere and for everyone’—that the integration of clinical information systems, mobile health (m‐health) tools, wearable devices, and artificial intelligence applications is crucial to strengthening healthcare services [7]. As a result, AI continues to gain importance both in everyday life and in the healthcare field. Health professionals are therefore expected to adopt AI‐supported technologies responsibly and to understand their implications for patient outcomes, healthcare delivery, and society as a whole [8]. Among these technologies, robot nurses emerging as a novel innovation in healthcare are believed to contribute significantly to improving efficiency, reducing human error, and advancing multiple aspects of clinical practice [9, 10]. Consequently, the progress of AI is directly linked to advancements in robotic systems [9]. In healthcare institutions, as medical information accumulates continuously and workloads increase, nurses may encounter challenges in recalling critical clinical data quickly and accurately. AI supports nurses in clinical decision‐making processes by using algorithms that identify the most relevant information [11]. In doing so, AI can help prevent diagnostic and treatment errors that may arise from human limitations. Moreover, recent studies have shown that robotic nursing systems and AI‐based applications reduce nurses' workload, enhance the quality of care, and minimise medical errors and inappropriate treatments. These technologies have already begun to shape contemporary nursing practice in meaningful ways [12, 13, 14, 15].
2. Background
In today's digitalised healthcare environment, robots have already become part of clinical practice, assisting healthcare professionals and contributing to greater efficiency and safety in nursing care [16]. To overcome misconceptions and biases stemming from fictional portrayals of robots, it is essential to enhance practical experience and promote education and awareness regarding the actual capabilities of care robots [17]. Since future nurses will likely collaborate with robotic systems, identifying nursing students' perspectives and understanding their potential concerns is of particular importance. Papadopoulos and Koulouglioti (2018) noted that healthcare workers hold diverse opinions about the role of robots in clinical care and that only a limited number have directly interacted with or observed such technologies [18]. Similarly, Yılmaz et al. (2021) explored the perceptions of health sciences students toward artificial intelligence and its use in healthcare. Their findings indicated that while students were aware of AI applications and expressed a willingness to use AI tools, they also exhibited uncertainty and limited knowledge in this area [10]. In another study, Akyüz et al. (2021) found that vocational health services students demonstrated insufficient understanding of AI and suggested that AI‐related topics should be incorporated into educational curricula [19].
AI has demonstrated superior accuracy and efficiency compared to humans in diagnosing and predicting treatment outcomes [20]. Nevertheless, there is broad agreement among scholars that AI‐driven robots are unlikely to replace nurses in the foreseeable future [6, 16, 21]. Supporting this view, Ergin et al. (2022) reported that most executive nurses in their study believed robots would not substitute for human nurses [22]. In contrast, Abdullah and Fakieh (2020) examined healthcare professionals' attitudes toward implementing AI technologies in Saudi Arabian healthcare institutions and found mixed perceptions regarding its integration [23]. Similarly, Taryudi et al. (2022) explored Indonesian nurses' perspectives on robotic utilisation during the COVID‐19 pandemic, revealing concerns that robots might eventually displace nurses or diminish their clinical competencies [24].
Accordingly, the present study seeks to investigate how educational interventions designed to raise nursing students' awareness about AI and robot nurses influence their anxiety levels toward these technologies.
Research Hypotheses:
The training provided for AI and robot nurses affects increasing the awareness level of nursing students.
The training provided for AI and robot nurses reduces students' anxiety towards AI.
3. Methodology
3.1. Study Design
This descriptive and cross‐sectional study was designed in accordance with the STROBE guidelines. The research was carried out in the Nursing Department of the Faculty of Health Sciences at a state university in Turkey. It adopted a quasi‐experimental pre‐test and post‐test design.
3.2. Setting and Sampling
The population of the study consisted of 342 nursing students, including first year (104), second year (87), third year (77) and fourth year (74) students studying between 2022 and 2023. The sample of the study was selected by stratified sampling method. In the stratified random sampling method, the population is divided into homogeneous subgroups (strata) from which participants are randomly selected. The method aims to increase representativeness [25].
The inclusion criteria were: (a) being 18 years or older, (b) being able to communicate in Turkish, and (c) voluntarily agreeing to participate in the study. The minimum required sample size was calculated as 176 participants, based on a 95% confidence level and a 5% margin of error using OpenEpi software (Version 3.01) https://www.openepi.com/SampleSize/SSPropor.htm). 32 students who provided incomplete responses were excluded from the analysis. Consequently, the final sample included 282 students: 84 first‐year, 70 s‐year, 58 third‐year, and 70 fourth‐year students.
3.3. Data Collection
Data were gathered between December 15, 2022, and October 15, 2023, from students studying in a Faculty of Health Sciences located in western Turkey. The data collection process was conducted through face‐to‐face interviews using the paper‐and‐pencil method. Before data collection, participants were informed about the purpose and procedures of the study, the confidentiality of their identities, and their right to withdraw at any stage. Written informed consent was obtained from all students.
Participants were asked to complete the Introductory Information Form, AI Knowledge Test, and AI Anxiety Scale. Following the pre‐test, all participants received a 45‐min educational session titled ‘AI Technologies and Robot Nurses,’ delivered via PowerPoint by the researchers.
The educational content covered the following topics: definition and historical background of AI, its place in scientific taxonomy, common AI technologies, examples of AI applications in daily life and healthcare, and current and potential roles of robot nurses in clinical practice. Immediately after the session, students were asked to complete the AI Anxiety Scale and AI Knowledge Test again.
3.4. Data Collection Tools
3.4.1. Introductory Information Form
Questions formed by the researchers in line with the literature [10, 22, 26] included 8 questions, 4 of which are about gender, age, class, willingly choosing the nursing department, AI and Opinions on Robot Nurses. It also includes a form consisting of 22 questions about their opinions on the applications of robot nurses in nursing care practices.
3.4.2. Artificial Intelligence Knowledge Test
This 20‐item test, prepared by the researchers in line with the literature [10, 22, 26], aimed to assess students' knowledge levels about AI and robot nurses. The maximum possible score was 100, while the minimum was 0. Higher scores indicated greater knowledge regarding AI and robotic nursing applications.
Prior to implementation, the test items were reviewed by ten subject‐matter experts to evaluate content validity. A content validity index (CVI) score was calculated, with at least 80% of items expected to be rated as ‘appropriate’ or ‘very appropriate’ [27]. The overall CVI was determined as 0.98. Subsequently, a pilot test was conducted with five students to assess item clarity; feedback was incorporated, but these data were excluded from the final analysis.
3.4.3. Artificial Intelligence Anxiety Scale
The Artificial Intelligence Anxiety Scale, originally developed by Wang and Wang (2022), was adapted into Turkish by Akkaya and Özkan (2021) [28, 29]. The scale includes 16 items across four subdimensions: Learning (items 1–5), Job Replacement (items 6–9), Sociotechnical Blindness (items 10–13), and AI Configuration (items 14–16). The Cronbach's alpha coefficient of the original scale was reported as 0.937, whereas it was found to be 0.904 in the present study.
3.5. Data Analysis
Data were analysed using the Statistical Package for the Social Sciences (SPSS) version 25.0. Descriptive statistics, including frequency, percentage, minimum and maximum values, mean, and standard deviation, were used to summarise the data. Reliability analyses were performed to assess the internal consistency of the scales.
For comparisons between two independent groups with normally distributed data, Independent Samples t‐tests were applied. For comparisons involving more than two groups, One‐Way ANOVA was used, and Bonferroni corrections were conducted when significant differences were found. Statistical significance was considered at p < 0.05 for all analyses.
4. Results
4.1. Descriptive Analysis of the Participants
Among the 282 participants, 68.8% (n = 194) were female, and 85.1% (n = 204) were aged between 18 and 25 years. First‐year students represented 29.8% (n = 84) of the sample, while 57.4% (n = 162) reported having chosen the nursing department voluntarily (Table 1).
Table 1.
Demographic distribution of the students participating in the study (n = 282).
| Demographic characteristics | Groups | n | % |
|---|---|---|---|
| Gender | Female | 194 | 68.8 |
| Male | 88 | 31.2 | |
| Age | 18–22 years | 240 | 85.1 |
| 23–26 years | 42 | 14.9 | |
| 1st year | 84 | 29.8 | |
| Grade | 2nd year | 70 | 24.8 |
| 3rd year | 58 | 20.6 | |
| 4th year | 70 | 24.8 | |
| Choosing the nursing department willingly | Yes | 162 | 57.4 |
| No | 120 | 42.6 | |
| Total | 282 | 100.0 |
Regarding students' views on AI and robot nurses, 83.7% (n = 236) stated that they had previously heard of these concepts, and 75.9% (n = 214) believed that AI and robot nurse topics should be incorporated into basic nursing education. Additionally, 84% (n = 237) thought AI‐based robots could contribute positively to nursing, while 87.2% (n = 246) reported not having taken any course related to AI or robotic nursing. Moreover, 78% (n = 220) believed that robots with AI would not replace human nurses (Table 2).
Table 2.
Opinions of students on artificial intelligence and robot nurses.
| YES | NO | |||
|---|---|---|---|---|
| n | % | n | % | |
| Have you heard of artificial intelligence and robot nursing concepts before? | 236 | 83.7 | 46 | 16.3 |
| Have you taken courses on artificial intelligence and robot nursing before? | 36 | 12.8 | 246 | 87.2 |
| Should basic education on artificial intelligence and robot nursing be included in university education programmes? | 214 | 75.9 | 68 | 24.1 |
| Do you think robots with artificial intelligence will replace nurses? | 62 | 22.0 | 220 | 78.0 |
| Do you think robots with artificial intelligence will benefit the nursing profession? | 237 | 84.0 | 45 | 16.0 |
In relation to nursing care practices, 91.8% of participants thought robot nurses should help prevent patient falls, 89.7% (n = 253) believed they should measure patients' height and weight, and 87.2% considered that robots could assist in changing bed linens. However, most students reported that AI‐based robots should not be responsible for invasive tasks such as blood sampling, intravenous line insertion, or medication preparation (Table 3).
Table 3.
Opinions of robot nurses about their practices in nursing care.
| Which practices should robot nurses perform in nursing care practices? | Agree | Not agree | ||
|---|---|---|---|---|
| n | % | n | % | |
| Prevent the patient from falling | 259 | 91.8 | 23 | 8.2 |
| Measuring the patient's vital signs | 231 | 81.9 | 51 | 18.1 |
| Measuring the patient's height and weight | 253 | 89.7 | 29 | 10.3 |
| Ensuring that the patient is fed at all meals | 176 | 62.4 | 106 | 37.6 |
| Provide ostomy care for the patient | 140 | 49.6 | 142 | 50.4 |
| To ensure the mobilisation of the patient | 191 | 67.7 | 91 | 32.3 |
| Changing patients' diapers | 225 | 79.8 | 57 | 20.2 |
| Bed baths for patients | 213 | 75.5 | 69 | 24.5 |
| Changing patients' bed linen | 246 | 87.2 | 36 | 12.8 |
| Changing the position of the patient | 217 | 77.0 | 65 | 23.0 |
| To make the dressing of patients | 116 | 41.1 | 166 | 58.9 |
| Providing education to patients (orientation, discharge, medication) | 149 | 52.8 | 133 | 47.2 |
| Taking anamnesis of patients | 135 | 47.9 | 147 | 52.1 |
| Providing emotional support to patients | 115 | 40.8 | 167 | 59.2 |
| Delivery of medical devices/materials | 224 | 79.4 | 58 | 20.6 |
| Making a call for patient transfer | 221 | 78.4 | 61 | 21.6 |
| Receiving the repaired devices | 242 | 85.8 | 40 | 14.2 |
| Going to the pharmacy to buy medicine | 228 | 80.9 | 54 | 19.1 |
| Dispensing oral medicines to patients | 160 | 56.7 | 122 | 43.3 |
| Opening an intravenous line for patients | 69 | 24.5 | 213 | 75.5 |
| Taking blood from patients | 68 | 24.1 | 214 | 75.9 |
| Measuring patients' blood sugar | 145 | 51.4 | 137 | 48.6 |
| Preparing medicines for patients | 112 | 39.7 | 170 | 60.3 |
The mean pre‐training AI Knowledge Test score was 63.08 ± 9.96, while the post‐training score increased to 66.70 ± 9.85, indicating a statistically significant difference (p < 0.05). Similarly, the mean AI Anxiety Scale score increased from 45.86 ± 11.18 before training to 48.10 ± 11.59 afterwards, also showing a significant difference (p < 0.05).
In the sub‐dimension analysis, the Learning subscale mean score rose from 11.34 ± 3.96 to 12.41 ± 4.07 (p < 0.05), and the AI Configuration subscale increased from 8.48 ± 3.26 to 8.89 ± 3.58 (p < 0.05) (Table 4).
Table 4.
Comparison of the knowledge test and artificial intelligence anxiety scale scores of the students before and after the training.
| Pre‐test | Post‐test | t | p | |||
|---|---|---|---|---|---|---|
| x̄ ± SD | Median (Lower‐Upper) | x̄ ± SD | Median (Lower‐Upper) | |||
| Knowledge test | 63.08 ± 9.96 | 70.0 (35–90) | 66.70 ± 9.85 | 75.0 (50–90) | −5.076 | 0.00* |
| AIAS Total | 45.86 ± 11.18 | 44.00 (32–78) | 48.10 ± 11.59 | 55.0 (32–80) | −3.715 | 0.00* |
| Learning Sub‐Dimension | 11.34 ± 3.96 | 11.0 (5–23) | 12.41 ± 4.07 | 13.0 (5–25) | 4.591 | 0.00* |
| Job Replacement Sub‐dimension | 12.51 ± 3.89 | 12.0 (7–20) | 12.55 ± 5.07 | 14.0 (8–20) | −0.175 | 0.86 |
| Sociotechnical Blindness Sub‐dimension | 13.52 ± 3.50 | 13.0 (8–20) | 13.73 ± 3.97 | 15.0 (8–20) | −0.993 | 0.32 |
| AI Configuration Sub‐Dimension | 8.48 ± 3.26 | 9.0 (3–15) | 8.89 ± 3.58 | 10.0 (6–15) | −2.121 | 0.03* |
p < 0.05, t‐test: independent sample t‐test.
4.2. Comparison of Anxiety Levels by Demographic Variables
Before training, the mean anxiety scores of female and male students were 47.07 ± 10.17 and 43.20 ± 12.81, respectively, indicating that female students exhibited significantly higher anxiety levels (p = 0.01). After the training, no statistically significant difference was found between females (47.86 ± 13.16) and males (44.64 ± 14.32) (t = 2.496, p = 0.06).
Comparing age groups, students aged 18–22 years had a mean pre‐training anxiety score of 46.72 ± 11.08, while those aged 23–26 years scored 40.95 ± 10.64, reflecting a significant difference (t = 3.132, p = 0.00). Post‐training scores were 47.21 ± 13.34 for the younger group and 44.80 ± 14.96 for the older group, with no significant difference (t = 1.059, p = 0.29).
There was no significant relationship between pre‐training AI anxiety scores and willingness to choose the department or class level (F = 0.518, p = 0.67; t = 0.052, p = 0.959). Similarly, post‐training comparisons revealed no significant differences in anxiety scores by gender, age, or department preference (t = 1.847, p = 0.06; t = 1.059, p = 0.29; t = 0.313, p = 0.755). However, a significant difference was found between class levels and post‐training anxiety scores (F = 6.416, p = 0.00). Post‐hoc analyses indicated that first‐, second‐, and third‐year students had significantly lower AI anxiety scores compared to fourth‐year students (Table 5).
Table 5.
Comparison of students' demographic distribution and mean scores of artificial intelligence anxiety scale (n = 282).
| Demographic characteristics | n | % | Pre‐test | Post‐test | |
|---|---|---|---|---|---|
| x̄ ± SS | x̄ ± SS | ||||
| Gender | Female | 194 | 68.8 | 47.07 ± 10.17 | 47.86 ± 13.16 |
| Male | 88 | 31.2 | 43.20 ± 12.81 | 44.64 ± 14.32 | |
| t: 2.496 | t: 1.847 | ||||
| p: 0.01* | p: 0.06 | ||||
| Age | 18–22 years | 240 | 85.1 | 46.72 ± 11.08 | 47.21 ± 13.34 |
| 23–26 years | 42 | 14.9 | 40.95 ± 10.64 | 44.80 ± 14.96 | |
| t: 3.132 | t: 1.059 | ||||
| p: 0.00* | p: 0.29 | ||||
| 1st grade | 84 | 29.8 | 44.72 ± 11.70 | 46.72 ± 11.07 | |
| Grade | 2nd grade | 70 | 24.8 | 46.40 ± 11.00 | 48.32 ± 11.21 |
| 3rd grade | 58 | 20.6 | 45.70 ± 10.76 | 44.29 ± 10.33 | |
| 4th grade | 70 | 24.8 | 46.82 ± 11.19 | 52.68 ± 12.51 | |
| F: 0.518 | F: 6.416 | ||||
| p: 0.67 | p: 0.00* | ||||
| 4 > 3,1,2 | |||||
| Choosing the Nurse Department willingly | Yes | 162 | 57.4 | 45.89 ± 10.80 | 48.29 ± 11.33 |
| No | 120 | 42.6 | 45.82 ± 11.73 | 47.85 ± 12.13 | |
| t: 0.052 | t: 0.313 | ||||
| p: 0.959 | p: 0.755 | ||||
| Total | 282 | 100.0 |
p < 0.05, t test: independent sample t test, F test: One‐way analysis of variance (ANOVA).
5. Discussion
This study represents the first attempt in both our country and the international literature to investigate nursing students' knowledge levels regarding AI and robot nurses. According to our findings, a statistically significant difference was found between students' pre‐training and post‐training mean knowledge scores (p < 0.05), indicating that their understanding of AI and robot nurses improved after the educational intervention. Supporting this finding, Ergin et al. (2023) also demonstrated a significant increase in knowledge scores among operating room nurses following similar AI and robot nurse training [26]. Only 12.8% of the participants in our study had previously received AI‐related education, most of which were single‐session trainings. Consistent with our results, previous studies reported that healthcare students are rarely exposed to AI‐focused education, generally possess limited knowledge, and express a strong need for additional training [30, 31, 32].
In recent years, rapid advancements in AI research have led to the widespread use of technologies that significantly influence daily life. Healthcare professionals are therefore expected to understand how to effectively integrate AI into patient care practices and to adopt these innovations without unnecessary apprehension [33]. In our study, the nursing students exhibited moderate anxiety levels toward AI and robot nurses after the training, with a statistically significant increase compared to pre‐training levels (p < 0.05). Contrary to our results, Nasreldin Othman et al. (2021) found that nurses' anxiety regarding collaboration with AI in patient care decreased significantly after training [34]. Similarly, Filiz et al. (2022) identified that healthcare workers were moderately concerned about AI in healthcare settings, attributing their anxiety primarily to insufficient information [35]. Comparable findings were also reported by Reyhan Aktaş and Dağlı (2023), who found moderate anxiety levels among midwifery students, and by Takıl et al. (2022), who observed similar anxiety among students from various professional fields [36, 37]. These studies, however, differ from ours in that they did not assess students' knowledge levels regarding AI.
Although integrating AI into higher education is recognised as essential, it has been reported that education and healthcare are among the sectors with the lowest adoption rates of AI [38]. This has been attributed to challenges in incorporating AI into educational curricula and developing intelligent teaching systems [39]. In our study, the mean score of the “learning” sub‐dimension of the AI anxiety scale was moderate, and anxiety levels significantly increased after the training (p < 0.05). These findings suggest that as students' awareness of AI and robot nurses improved, their learning‐related anxiety also rose, possibly due to recognising their knowledge gaps. Learning anxiety reflects a lack of confidence in understanding complex topics [40]. Since learning AI requires comprehending intricate algorithms and systems, it can naturally induce anxiety among students. Similarly, ‘job replacement anxiety’ denotes the fear that AI technologies might replace existing human roles in healthcare [29, 40].
It is widely acknowledged that robots can assist with various nursing tasks such as patient transfer, hygiene care, dressing, IV insertion, and vital sign monitoring [16]. For instance, Robear, a robot designed for physically demanding tasks, helps patients stand up and move between locations [9, 16]. Likewise, IV Robots RIVA ensure patient safety and cost efficiency in drug administration by minimising dosage and infusion errors [9, 41]. According to our findings, nursing students mostly believed that robot nurses should assist in tasks such as preventing falls, measuring body weight and height, and changing bed linens. However, more than half opposed assigning robots to procedures involving blood sampling, drug preparation, or providing emotional support, reflecting concerns about human touch and empathy in patient care.
Furthermore, our results indicated that female nursing students exhibited significantly higher anxiety toward AI before training compared to male students (p < 0.05). Supporting our study result [32, 42] reported that male students had a more positive attitude towards artificial intelligence than female students. In our research, as a result of the training provided to students regarding artificial intelligence and robot nurses, artificial intelligence anxiety levels of 4th‐grade students were higher (p < 0.05). In studies conducted with nursing [32] and health students [43], it was reported that artificial intelligence attitudes developed positively as the class level progressed.
6. Implications for Nursing and Health Policy
The findings of this study provide critical insights into the intersection of AI, nursing education, and health policy. The significant increase in both knowledge and anxiety levels among nursing students following the AI‐focused training intervention suggests that while educational initiatives enhance cognitive understanding, they may concurrently heighten emotional apprehension—particularly regarding the perceived threat of AI to professional roles and the complexity of AI systems.
In light of these outcomes, it is imperative for nursing education programmes to proactively integrate AI and robotics content into their curricula. This integration should not be limited to theoretical instruction but must also incorporate experiential learning opportunities, such as simulations and interactive modules, to foster both familiarity and confidence. Furthermore, educational strategies must address the emotional and psychological dimensions of technological adaptation, with particular emphasis on managing AI‐related anxiety and misconceptions.
From a policy perspective, governing bodies in nursing and healthcare must recognise AI literacy as an essential competency for future nurses. Health policy frameworks should support curricular reforms that promote digital preparedness and provide resources for faculty development, infrastructure enhancement, and ongoing evaluation of educational effectiveness. Moreover, ethical considerations, such as data privacy, decision‐making accountability, and the nurse‐patient relationship in AI‐supported environments, must be embedded within regulatory guidelines.
Overall, preparing the nursing workforce to engage effectively and critically with AI technologies is essential for safeguarding the quality of care, advancing health system innovation, and ensuring the profession's relevance in a rapidly evolving digital landscape.
7. Limitations and Recommendations
Although this study contributes to the literature, it has some limitations. Firstly, this study was conducted in a single university in western Turkey. Therefore, the results cannot be generalised to nursing students in Turkey and the world. Secondly, we lost data due to the natural disaster, an earthquake, during our study.
It is recommended to conduct studies in which objective measurement methods are utilised instead of relying only on the self‐reports of the participants.
8. Conclusion
As a result, it was demonstrated that students' knowledge level and anxiety towards AI increased as a result of the training provided for artificial intelligence and robot nurses. It was determined that the anxiety of the students towards AI was at a moderate level, and the anxiety of women and senior students was higher. These findings emphasise the importance of education programs that will strengthen nursing students' preparation for artificial intelligence implementations, and it is recommended that nursing curricula be updated to address these new technological demands.
Author Contributions
Conceptualisation: Eda Ergin. Data curation: Narin Yildiz and İrem İncekara. Formal analysis: Eda Ergin, Narin Yildiz and İrem İncekara. Investigation: Eda Ergin. Methodology: Eda Ergin, Narin Yildiz and İrem İncekara. Validation, Visualisation: Eda Ergin. Roles/Writing – original draft, Writing – review and editing: Eda Ergin, Narin Yildiz and İrem İncekara.
Ethics Statement
We complied with all the ethical standards established in the Declaration of Helsinki at all stages of the study. Before we conducted the study, we obtained written ethical approval to conduct the study from Izmir Bakırçay University, Non‐Interventional Ethics Committee (decision number: 763, decision year: 2022), and written institutional permission from the institution where the study was to be conducted (decision number: E‐99838568‐000‐2200037720, decision year: 2022). Finally, written informed consent was obtained from the students to participate in the study after they were informed about the study. In addition, confidentiality and anonymity were ensured in the research.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
The authors thank all of the students for their participation in this research. This study has been accepted by ‘TÜBİTAK 2209‐A ‐ Research Project Support Programme for Undergraduate Students’ as a research project numbered 1919B012200902 within the scope of 1 semester of 2022. The budget approved for our project was 4.000 Turkish liras.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.