Content area
Aim
The aim of this study is to examine the preparedness levels of nursing students for Medical Artificial Intelligence and Artificial Intelligence Anxiety.
Design
This is a descriptive mixed-method study.
Methods
The medical artificial intelligence readiness scale, artificial intelligence anxiety scale and semi-structured interview form were applied to the nursing department students of two separate faculties (n:358). The focus group interview was conducted with 19 participants. Quantitative and qualitative data were analyzed separately and combined.
Results
The average artificial intelligence awareness score of all the participants participating in the study was 75.05 ± 11,805, and the average artificial intelligence anxiety score was 49.59 ± 11,263. The average artificial intelligence awareness score of the participants in the qualitative part of the study was 72.89 ± 10.519 and the average artificial intelligence anxiety score was 70.68 ± 5.165. 145 opinions were identified regarding the concerns of individuals with artificial intelligence anxiety. These opinions are grouped under four sub-themes; Two sub-themes were evaluated as "complementary" and the other two as "convergent" with quantitative data. Sequential explanatory design was used in the study. After the quantitative data were evaluated, qualitative data were collected, evaluated and the results were integrated.
Conclusion
It was determined that the students were ready for artificial intelligence; they believed that artificial intelligence increases productivity and acts as a facilitator; but they also had various concerns about artificial intelligence. Qualitative and quantitative data supported each other.
Patient or public contribution
Increasing the awareness of nursing students and nurses on artificial intelligence can contribute to public health.
Clinical trial registration
NCT06623448.
1 Introduction
Artificial intelligence (AI) is a subfield of computer science that enables the development of intelligent programs capable of thinking, behaving, and making decisions based on stored memory, similar to human cognition. Today, AI is applied across various fields such as communication, transportation, healthcare, education, security, entertainment, business, and commerce ( Bhbosale et al., 2020). AI is used in patient-oriented areas such as early detection, diagnosis, treatment recommendations, patient follow-up, identification of risks in the field of health ( Alowais et al., 2023), and in managerial tasks in the management process of health institutions ( Secinaro et al., 2021). In general, the areas of use of AI in healthcare are document management, telehealth, cost and quality management, digital healthcare, early diagnosis, diagnostic and treatment procedures, research and education, drug production and calculations, epidemic prediction, radiology, improving the quality and efficiency of care, identifying high-risk patients ( Pepito and Locsin, 2019).
1.1 Background
In the field of health, the use of AI in nursing is very important. Because the use of AI improves patient care quality, workflows and education processes ( Martinez-Ortigosa et al., 2023; von Gerich et al., 2022). The use of AI in education improves nursing students' knowledge acquisition, clinical decision-making skills and practical experiences ( Lifshits and Rosenberg, 2024). However, it was explained that the use of AI is not included as a course in the nursing education curriculum, but it should be added to the curriculum ( Simms, 2025; Sun et al., 2025).
Studies conducted with students on AI yield different results; in the study by Bodur et al. (2022), nursing students recognized both the positive and negative aspects of AI ( Bodur et al., 2022). According to a study conducted with health sciences faculty students, it was noted that while students are aware of the importance of AI in healthcare and are willing to use AI tools, they also experience anxiety and a lack of knowledge ( Yılmaz et al., 2021). It was reported that nursing students in the Philippines had a moderate level of readiness for AI ( Labrague et al., 2023). AI is a tool that can profoundly impact nursing ( Van Bulck et al., 2023). Indeed, today's nursing students have the potential to actively use the increasingly prevalent AI applications in the future delivery of healthcare services. Therefore, it is believed that evaluating nursing students' awareness of medical AI and their AI-related anxieties will contribute to the field. The results of this study may guide the future education and skill development of nursing students who have the potential to use AI in healthcare services.
1.2 The study
The aim of this study was to examine nursing students' readiness levels for medical AI and their anxiety about AI using a mixed-method approach.
2 Methods/methodology
2.1 Design
This is a descriptive mixed-method (explanatory sequential) study. Support was received from the trans theoretical model. This model can be used for behavior change in nursing education. According to this model, the readiness of the participants is first assessed, and the educational content is determined, implemented and evaluated accordingly. In this way, it may be possible to evaluate and support students individually ( Keshmiri et al., 2017; Lee et al., 2015). The inferences obtained from this study will provide situation determination for nursing students and contribute to the use of the results in nursing education.
Research Questions:
Regarding nursing students:
1. What is their level of awareness of medical AI?
2. What is their anxiety level related to AI?
3. Is there a relationship between their awareness of AI and AI-related anxiety levels?
4. What are the views of those experiencing AI-related anxiety about artificial intelligence?
2.2 Population and sample
The population of the study consisted of nursing students. The sample of the study consisted of students from the Nursing Department of XXX University's Faculty of Health Sciences and XXX University's Faculty of Nursing. The faculty of nursing is located in a provincial center and the faculty of health sciences is located in a district center.
In a related study, it was reported that the mean artificial intelligence anxiety (AIA) scores of nurses differed between those who had knowledge of AI (t = 2.45 ± 0.61, n = 43) and those who did not (t = 2.85 ± 0.69, n = 76) ( Çobanoğlu and Oğuzhan, 2023b). Based on the data from this study, a power analysis was conducted using the G*power 3.1.4 software. The minimum sample size was determined to be 353, with an effect size of 1.175, a power of 0.95, and a margin of error of 0.05. The present study was completed with 358 participants. The post-hoc power of the study was 0.952.
A total of 977 students from the Faculty of Nursing at XXX University and the Nursing Department of XXX University's Faculty of Health Sciences were included in the study. Students who did not consent to participate were excluded.
2.3 Data collection techniques and tools
Data were collected using the Student Information Form, the Medical Artificial Intelligence Readiness Scale (MAIR), the Artificial Intelligence Anxiety Scale (AIA), and a Semi-Structured Interview Form (July August 2024).
2.3.1 Student information form
This form consists of 8 questions, including students' sociodemographic information, whether they have knowledge about AI, internet usage, and their school and class details. The form was developed based on the relevant literature ( Aslan and Subaşı, 2022; Bodur et al., 2022; Yılmaz et al., 2021).
2.3.2 Medical artificial intelligence readiness scale
The scale was developed by Karaca et al. (2021). Initially applied to medical students, it has since been used in other health-related fields as well ( Büyükkaya et al., 2023). The scale measures the level of readiness for medical artificial intelligence in healthcare. However, it does not categorize readiness as appropriate/inappropriate or sufficient/insufficient. The scale consists of four factors: cognitive (items 1–8), skill (items 9–16), foresight (items 17–19), and ethics (items 20–22). The score ranges for the factors are as follows: cognitive: 8–40; skill: 8–40; foresight: 3–15; ethics: 3–15. The total score ranges from 22 to 110. Higher scores indicate a higher level of readiness. The items are answered on a 5-point Likert scale (1-Strongly disagree, 5-Strongly agree). The Cronbach's alpha coefficient of the original scale was 0.87 ( Karaca et al., 2021). In the present study, the Cronbach's alpha coefficient was 0.93.
2.3.3 Artificial intelligence anxiety scale
The scale was developed by Wang and Wang (2022) and adapted into Turkish by Akkaya et al. (2021). The original scale consists of 21 items, while the Turkish version includes 16 items across 4 factors. It is a 5-point Likert-type scale (1-Strongly disagree, 5-Strongly agree). The sub-dimensions of the scale are learning (items 1–5), job transition (items 6–9), socio-technical blindness (items 10–13), and artificial intelligence configuration (items 14–16). The lowest score that can be obtained from the scale is 16 and the highest score is 80. Higher scores indicate increased AI anxiety. The Cronbach’s alpha coefficient of the scale was 0.95 ( Akkaya et al., 2021). In the present study, the Cronbach's alpha coefficient was 0.93.
2.3.4 Semi-structured interview form (SSIF)
The form was prepared based on the literature and consists of three questions: Are we ready to use AI? What are your thoughts on the use of AI? How does the use of AI in nursing make you feel?) ( Aslan and Subaşı, 2022; Bodur et al., 2022; Yılmaz et al., 2021).
2.3.5 Data collection
Quantitative data were collected online. To prevent repeated submissions, each user was allowed to fill out the form only once. Completing the forms took approximately 10 min. After collecting the quantitative data, individuals with the highest AI anxiety scores were identified. Since the AIA scale does not have a cutoff point, 30 participants with scores greater than 65 were deemed suitable for qualitative interviews. It is known that collecting qualitative data from 5 to 25 participants in purposive sampling is sufficient ( Creswell, 2021). Focus group interviews were conducted with students who scored greater than 65 in AIA scale until data saturation was reached. Focus group interviews were completed with 19 students. Audio recordings and written notes were taken during the interview, and the recordings were transcribed within the first 48 h post-interview to ensure no data loss. Each focus group interview lasted approximately 60 min. Days and times were scheduled for the interviews based on the participants' availability. Each participant was interviewed once, and the interviews were completed over a total of four separate sessions.
2.4 Statistical analysis
Since the research included qualitative and quantitative data, separate analyses were conducted for each area. Statistical Package for Social Sciences (SPSS) version 29.0 was used for the evaluation of the quantitative data. Continuous variables were presented as mean and standard deviation and categorical variables were presented as number and percentage. The normality distribution of continuous dependent variables was assessed using Skewness and Kurtosis values (-2/+2) and by examining the histogram for a bell-shaped curve, allowing for the use of parametric tests. The homogeneity of variances was determined using Levene's Test statistics. In the analyses, Independent Samples t-test and One-way ANOVA were conducted, while Welch's test (Robust test in one-way heteroscedastic ANOVA) was utilized in cases where variances were not homogeneously distributed. To determine the source of differences between groups, post hoc multiple comparison tests including Bonferroni and Tamhane’s T 2 were employed. The relationships between continuous variables were evaluated using Pearson Correlation analysis, with correlation coefficients categorized as weak (r = 0.1–0.3), moderate (r = 0.3–0.7), and strong (r = 0.7–1.0). Reliability analyses for overall scale scores were calculated using Cronbach's α method. A p value of <0.05 was accepted as statistically significant in all analyses.
For the evaluation of qualitative data, Colaizzi’s seven-step phenomenological interpretation method was employed ( Morrow et al., 2015). The evaluation of the statements consisted of three stages. These included rereading for organizing descriptive notes (associating and interpreting), rereading to relate the data, and defining categories to abstract the data. The analyses were first conducted individually by each researcher and then collaboratively evaluated. Any disagreements among the researchers were discussed and finalized with input from a faculty member specialized in qualitative research. To ensure reliability, Miles-Huberman agreement analysis was conducted for the themes determined by SSIF and the coders. The agreement coefficient was calculated as 1 for SSIF and 0.97 for the themes identified by the coders. Maxqda Analytics Pro 2020 statistical software was utilized in the analyses.
A convergence matrix was designed to integrate the findings on quantitative and qualitative data ( O’Cathain et al., 2010). The opinions of participants regarding AI in the qualitative data were sought within the quantitative data. Subsequently, to continue the grouping, additional dimensions in the quantitative data were re-examined; (1) Convergent: "Qualitative and quantitative research results are consistent", (2) Complementary: "Qualitative and quantitative research results are complementary in nature."
A sequential explanatory design was used to implement integration in the mixed method. First, quantitative data were collected and analyzed. Following this, qualitative data were collected from eligible participants. These data were then brought together, evaluated and integrated. he process is given in
3 Results
Sociodemographic data of the participants, the distributions of scale scores, and the relationships between the scale scores were evaluated in order, and are presented below along with tables.
The mean age of all participants in the study was 20.70 ± 1.427 years, the mean MAIR scores of the participants were 75.05 ± 11.805, and the mean AIA scores of the participants were 49.59 ± 11.263. Most of the participants were female (82.2 %) and first-year students (37.3 %). All participants used the internet for >60 min per day. The mean age of those participating in the qualitative part of the research was 20.53 ± 1.467, the mean MAIR score was 72.89 ± 10.519, and the mean AIA score was 70.68 ± 5.165. The characteristics of the participants in the qualitative and quantitative sections of the study were similar except for the AIA score. Other characteristics of the participants are presented in
No difference was found in MAIR scores with respect to independent variables. Total AIA score was higher for females (50.12 ± 11.587), fourth-year students (53.12 ± 9.546), those using the internet for more than 241 min per day (51.02 ± 12.109), participants with knowledge about AI (50.11 ± 11.385), and those with a fear of AI for the nursing profession (54.92 ± 11.879), with the differences being statistically significant (p < 0.05) (
No correlation was found between the total scores of the two scales (
Findings related to qualitative data are presented along with a concept map, coding matrix, and integration table, showcasing participants' views under relevant themes.
The code hierarchy created based on the views of individuals experiencing AI anxiety presents four themes and 21 sub-themes. Among the themes; “readiness” consists of six, “increased efficiency” consists of five, ‘facilitator’ consists of four, and “anxiety” consists of 11 sub-themes (
The themes and sub-themes created based on the opinions of individuals with AI anxiety are presented in the coding matrix. The total number of opinions obtained from the participants is 145. It was seen that the sub-themes with the highest frequency were learning (20), workload (16) and ease of use (16). Other sub-themes and their numbers are presented in the matrix (
In the integration of qualitative and quantitative data, it was observed that the themes of "readiness" and "increased efficiency" converged with quantitative data. However, in the themes of "facilitators" and "anxiety," complementary results were obtained when compared to the quantitative data. Details are presented in
3.1 Themes
The statements of the participants are presented separately under four themes below. The statements were coded (P1, P2, etc.) without revealing personal information and were explained in conjunction with the quantitative data.
3.1.1 Theme 1: readiness
The qualitative data of the study were shaped based on interviews with participants who had high AIA scores. The first theme was identified as "readiness". Nursing students expressed that they were ready to use AI technologies. Similar studies also indicate that students feel ready to use AI (
Demir Kaymak et al., 2024; Yalcinkaya et al., 2024). In the present study, the MAIR scores of the students and the fact that 83 % of the group had knowledge in this area converge with the "readiness" theme identified in the qualitative interviews (
Table 4).
P.16. "Nurses and students are ready, but the public needs to learn first. For example, there’s a learning process before using robotic vacuum cleaners, and this is the same. The public will be ready after they learn, but they are not ready yet."
P.18. "We are ready and already using it. Nurses are also ready; they all use smartphones, so they are ready for this. The public is ready as well. Except for the very elderly… They won't use it anyway, they don't need it."
3.1.2 Theme: increased efficiency
Students argued that AI increases efficiency by making care easier, reducing workload, improving medication tracking, and lowering the risk of complications. AI offers ways to reduce healthcare costs and enhance the efficiency of healthcare services (
Matheny et al., 2019). The use of AI-supported technologies has been reported to improve and facilitate the nursing workforce, time management, and patient monitoring (
Mao and Luo, 2024). It is also noted that AI enables remote patient monitoring and contributes to wound care (
Barakat-Johnson et al., 2024). Additionally, AI provides a convenience by reducing the time nurses spend on documentation, allowing them to dedicate more time to patient care (
Yadav, 2024). High mean MAIR scores converge with the students' views that the use of AI can contribute to efficiency (
Table 4).
K.10. "It makes care easier. For example; Pressure sores can be reduced. For example; changing the patient's position every two hours for pressure sores could be easier. AI can time it, while we sometimes miss it or don't prioritize it. But if AI follows orders, there won't be any delays."
P.13. "In particular, tracking the side effects of medications could become easier and the risk of complications might be reduced. We can teach it the procedures, it will apply them, and we will oversee it. It can monitor the patient and assist us in providing care. It could help reduce certain risks, such as the risk of falls. It can monitor the Glasgow scale, facilitate compliance with infection control measures…"
P.9. "But if there is an AI that knows its job well, it would make it much easier. For example, I am an orthopedic nurse, and we have a diabetic patient. If AI were integrated into the hospital system and could inform me that I need to send this patient for diabetes education, it would be very helpful. I think such patients often get overlooked, this could prevent that."
3.1.3 Theme 3: anxiety
The primary concerns of nursing students regarding AI are the potential for AI to replace the nursing profession, leading to unemployment, and the possibility of privacy issues for patients. In a similar study, it was reported that nursing students had high levels of artificial intelligence anxiety (
Yigit and Acikgoz, 2024). In contrast, other studies reported that AI cannot replace nurses (
Hote et al., 2023). However, concerns remain about privacy and security issues for patients (
O’Connor et al., 2023). It is recommended that nurses gain knowledge and skills in AI applications. AI should not replace nursing care but should support its processes. To achieve this, it is important to reduce AI anxiety (
Mohanasundari et al., 2023). The mean AIA score of all participants in the present study was 49.59 ± 11.263. The AIA score ranges from 16 to 80 (
Akkaya et al., 2021). Therefore, it can be said that the participant group generally experiences AI anxiety. This indicates that the qualitative and quantitative data complement each other (
Table 4).
P.12. "But there are also disadvantages. For example; If a prescribed medication is written incorrectly (such as the dosage), and AI, in the form of a robot, directly administers it, it could bypass our control. I actually worry about such critical situations slipping out of control."
P.14. "However, my biggest concern here is privacy. When I think about providing body care to patients alongside AI, if home cleaning robots can record us through their cameras, imagine the immense risk for a naked patient. For this reason, control should not be entirely handed over to AI; there should always be human oversight."
P.4. "I watch Tesla's robot videos, and it actually scares me. I worry about what will happen if they take over the world."
P.11. "If the public encounters robots in areas where AI is used in hospitals and if most tasks are performed by them, people might start saying, 'nurses are getting paid for nothing.' This could have a negative impact. It increases my professional anxiety. I provide care, but if a robot can do it better than me, society might accept them more."
3.1.4 Theme: facilitator
Students emphasized that AI reduces their workload and facilitates their tasks by allowing easier access to information while doing homework and studying. In a study conducted with students in the healthcare field, it was reported that 70 % of students used AI technologies for their assignments, and that these technologies are valuable tools for accessing clinical and educational medical information, thus making their work easier (
Cherrez-Ojeda et al., 2024). AI is also an important tool that facilitates and supports nurses in their work (
Hote et al., 2023; O’Connor et al., 2023). It can be said that the "facilitator" theme complemented the MAIR scores obtained from the students (
Table 4).
P.12. "It provides systemic convenience. The advantage is that it makes our work easier, reduces the workload…"
P.4. "It helps both our and your development at school. Because our teachers may also need to access new information, AI can provide that directly. Both teachers and students can access innovations more easily here."
P.14. "It is useful in every field of education. Whether doing homework, studying for classes, or preparing for exams… ultimately, it will facilitate our learning in class.”.
4 Discussion
In this study, the MAIR and AIA scores of nursing students were examined using a mixed-method approach. To the best of our knowledge, this is the first study of its kind in the field and will provide significant contributions to the literature.
According to the quantitative results of the study, students' MAIR scores are at a moderate level, and sociodemographic characteristics do not influence MAIR scores. Similarly, nursing and midwifery students have also been reported to have moderate MAIR levels ( Demir Kaymak et al., 2024). Contrary to our findings, one study reported that fourth-year students have higher MAIR scores compared to second-year students, and those wishing to receive education about AI have higher MAIR scores ( Yalcinkaya et al., 2024). In the present study, 83.8 % of the students had knowledge about AI. However, this knowledge did not affect MAIR scores. Hamedeni et al. (2023) reported that the attitudes of nurses and doctors who had previously received education and had knowledge about AI were not affected by this knowledge ( Hamedani et al., 2023).
In this study, although there was no difference in students' MAIR scores, there was a difference in AI anxiety scores according to gender. In the literature, it has been explained that gender does not have a direct effect on AI anxiety ( Migdadi et al., 2024), but the fact that female students feel more prone to use technological innovations reduces AI anxiety ( Akkaya et al., 2024; Salameh et al., 2025). However, in a study conducted with nursing students, it was reported that women interpreted the profession as “helping” and “giving care” while men were more interested in technical and managerial roles ( Prosen, 2022). Similarly, in the student group in this study, it can be thought that women perceived themselves as more distant from technical issues professionally and had more AI papers.
Another factor affecting students' AI anxiety is the year of education. Fourth grade students were found to have higher anxiety. In the literature, it is stated that the year of education of students in Turkey does not affect AI anxiety ( Demir Kaymak et al., 2024; Ongün et al., 2024). It was emphasized that the factor affecting AI anxiety is mostly related to the personal characteristics of the student and his/her feeling of readiness for using AI ( Demir Kaymak et al., 2024; Ongün et al., 2024; Soyer Er and Ozpinar, 2025; Varol, 2025). Unlike the students in this study, both MAIR and AI anxiety scores were higher. It is thought that the difference determined in the students is due to their personal characteristics.
This analysis determined that AI anxiety in nursing students was related to the duration of internet use and knowledge about AI. In this study, it was observed that those who had more internet usage time and knowledge about AI had higher levels of AI anxiety. In the literature, AI anxiety is not directly associated with internet usage time. However, it is explained that internet usage time may increase anxiety indirectly by affecting the attitude towards AI ( Firth et al., 2024; Jiang and Qi, 2024). Having knowledge about AI decreases AI anxiety ( Kaya et al., 2024). However, individuals may be affected differently by the information they acquire according to their AI literacy ( Schiavo et al., 2024). In this study, the content of the information about AI acquired by those who used the Internet for a longer period of time may have increased their AI anxiety.
Students' knowledge about AI has a positive effect on their AI anxiety ( Taskiran, 2023). For this reason, it is stated that a content about AI should be included in the nursing education curriculum ( Lifshits and Rosenberg, 2024). In the present study, no separate education was provided to students; instead, their existing knowledge was evaluated. Differences in the results of other studies for students who took AI courses may have stemmed from differences in learning motivation, the quality of education, content, and duration. Additionally, factors such as students' age, internet usage, and previous education may have influenced the results.
According to the results of the present study, students' AIA scores are at a medium level, with higher scores observed among women, fourth-year students, those with high daily internet usage, individuals with knowledge about AI, and those who fear the impact of AI on the nursing profession. In a study conducted in Turkey, it was found that nurses with lower education levels, no knowledge of AI technologies, and beliefs that AI technologies would not have a positive effect on patient care had higher AIA scores ( Çobanoğlu and Oğuzhan, 2023a). In another study conducted with midwifery and nursing students, it was found that AIA scores were affected by MAIR levels and perceptions of AI as a threat to their profession ( Yalcinkaya et al., 2024). Similarly, in a study conducted with medical students, students stated that they were concerned about AI taking over their profession ( Swed et al., 2022). Increasing knowledge levels regarding AI can reduce anxiety levels ( Özbek Güven et al., 2024). Therefore, the use of AI technologies, which have entered our lives with advancing technology, in nursing is inevitable. Perceptions, anxieties, and acceptance of AI are significant factors influencing the intention to use AI, and it is recommended that systematic training programs be planned to improve attitudes toward AI ( Eyüp and Kayhan, 2023). The increased awareness of nursing students regarding AI may be increasing their AI levels. It is essential for students to possess adequate knowledge about AI.
In the present study, no significant correlation was found between the students' MAIR and AIA levels. Similar to the results of the present study, another study reported no significant relationship between AIA and MAIR among nursing and midwifery students ( Demir Kaymak et al., 2024). Another study reported a negative relationship between AIA and attitudes towards artificial intelligence ( Eyüp and Kayhan, 2023).One study showed that those lacking knowledge about AI exhibited higher levels of fear and anxiety ( Filiz et al., 2022). Another study explained that taking an AI nursing course positively affected students' readiness for AI ( Taskiran, 2023). It can be said that obtaining accurate information about the subject and taking a course about AI affect students' readiness for AI. The difference observed in the literature and the results of this study may be due to the differences in students' access to technology and in the education curriculum.
5 Conclusions and recommendations
The students' MAIR scores were moderate and their sociodemographic characteristics did not affect the MAIR scores. AIA scores were moderate, and were higher among females, fourth-year students, those with high daily internet usage, those with knowledge about AI, and those who expressed concerns about the nursing profession. There was no significant correlation between MAIR and AIA levels. It was determined that the students were ready for AI; they believed that AI increased efficiency and acted as a facilitator; but they also had various concerns about AI. Qualitative and quantitative data supported each other. The use of AI technologies in the field of health and education is inevitable; therefore, it may be recommended to use educational methods such as simulation, interprofessional learning, case-based learning, etc. in nursing students' curricula that can reduce AI anxiety and increase readiness; and to continue education and learning on the use of medical AI technologies after graduation.
5.1 Limitations of the study
The limitations of the study are that the data were collected online and only from students of schools in one city, the sample was not randomly selected, and only students with high anxiety scores were included in the focus group.
5.2 Strengths of the study
The study was conducted using a mixed-method research approach
CRediT authorship contribution statement
Burucu Rukiye: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Türkben Polat Hilal: Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
Patient consent statement
Informed consent was obtained from all individual participants included in the study.
Ethical dimension of the research
Research permission was obtained from the Ethical Committee for Scientific Research of Necmettin Erbakan University (APPROV NO: 05.06.2024/ 2024/876), and implementation permission was obtained from the Necmettin Erbakan University, Seydişehir Kamil Akkanat Faculty of Health Sciences administration (ARRROV NO: 20.06.2024/ E-85308421-100-522733) and Necmettin Erbakan University, Nursing Faculty administration (ARRROV NO: 30.06.2024/ E- 85308421-100-526861) Participants were informed about the subject, and their consent was obtained online. The research was conducted and reported in accordance with the Helsinki Declaration and the MMR-RHS checklists. Both qualitative and quantitative data of individuals were stored encrypted on the researcher’s computer.
Permission to reproduce material from other sources
Permission for use has been obtained from the authors by mail for the scales used.
Funding statement
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. However, Necmettin Erbakan University will provide support in the publication process of the article within the scope of Wiley- TUBITAK/ Turkey agreement.
Declaration of Competing Interest
The authors have no relevant financial or non-financial interests to disclose.
Table 1
| Variables | Quantitative data | Qualitative data | ||
| Mean ± SD | Min-Max | Mean ± SD | Min-Max | |
| Age | 20.70 ± 1.427 | 18–24 | 20.53 ± 1.467 | 18–23 |
| MAIR Tolat | 75.05 ± 11.805 | 35–110 | 72.89 ± 10.519 | 50–95 |
| AIA Total | 49.59 ± 11.263 | 16–80 | 70.68 ± 5.165 | 65–80 |
| n | % | n | % | |
| Gender | ||||
| Female | 295 | 82.2 | 16 | 84.21 |
| Male | 64 | 17.8 | 3 | 15.79 |
| Educational status (Year) | ||||
| 1st | 134 | 37.3 | 8 | 42.1 |
| 2nd | 91 | 25.3 | 3 | 15.8 |
| 3rd | 84 | 23.4 | 5 | 26.3 |
| 4th | 50 | 13.9 | 3 | 15.8 |
| Daily internet use (Minute) | ||||
| 60–120 | 44 | 12.2 | 0 | 0 |
| 121–180 | 74 | 20.6 | 2 | 10.5 |
| 181–240 | 90 | 25.1 | 14 | 73.7 |
| >241 | 151 | 42.1 | 3 | 15.8 |
| Presence of knowledge about AI | ||||
| Yes | 301 | 83.8 | 17 | 89.5 |
| No | 58 | 16.2 | 2 | 10.5 |
| Using AI for lessons | ||||
| Yes | 214 | 59.6 | 10 | 52.6 |
| No | 145 | 40.4 | 9 | 47.4 |
| Being afraid of AI | ||||
| Yes | 195 | 54.3 | 18 | 94.7 |
| No | 164 | 45.7 | 1 | 5.3 |
| Fear of AI for the nursing profession | ||||
| Yes | 117 | 32.6 | 16 | 84.2 |
| No | 242 | 67.4 | 3 | 15.8 |
Table 2
| Variables | MAIR Tolat | AIA Total | ||||
| Mean | SD | t/p | Mean | SD | t/p | |
| Gender | ||||||
| Female | 75.24 | 11.573 | 0.336 | 50.12 | 11.587 | 4.468 |
| Male | 74.16 | 12.881 | 0.562* | 47.16 | 9.329 | 0.035* |
| Educational status (Year) | ||||||
| 1st | 75.63 | 12.752 | 0.669 | 49.87 | 11.817 | 3.008 |
| 2nd | 74.62 | 12.117 | 0.572** | 47.26 | 11.435 | 0.030** |
| 3rd | 73.80 | 10.431 | 49.57 | 10.691 | ||
| 4th | 76.36 | 10.823 | 53.12 | 9.546 | ||
| Daily internet use (Minute) | ||||||
| 60–120 | 80.14 | 14.016 | 2.198 | 45.09 | 11.581 | 2.981 |
| 121–180 | 73.66 | 11.228 | 0.069*** | 49.93 | 10.748 | 0.033*** |
| 181–240 | 74.81 | 9.282 | 49.11 | 9.477 | ||
| >241 | 74.38 | 12.443 | 51.02 | 12.109 | ||
| Presence of knowledge about AI | ||||||
| Yes | 76.21 | 10.901 | 1.928 | 50.11 | 11.385 | 5.100 |
| No | 69.02 | 14.345 | 0.166* | 46.91 | 10.293 | 0.025* |
| Using AI for lessons | ||||||
| Yes | 77.63 | 10.690 | 3.294 | 49.38 | 10.972 | 0.028 |
| No | 71.23 | 12.365 | 0.070* | 49.90 | 11.712 | 0.868* |
| Being afraid of AI | ||||||
| Yes | 73.91 | 11.363 | 0.047 | 54.66 | 10.319 | 1.239 |
| No | 76.40 | 12.207 | 0.829* | 43.57 | 9.203 | 0.266* |
| Fear of AI for the nursing profession | ||||||
| Yes | 72.37 | 10.837 | 0.724 | 54.92 | 11.879 | 4.542 |
| No | 76.34 | 12.055 | 0.395* | 47.01 | 10.003 | 0.034* |
Table 3
| Variables | MAIR Total | |
| AIA Total | r | −.040 |
| p | .447 |
Table 4
| Main theme | Subtheme | Qualitative interviews | Quantitative data | Evaluation |
| Readiness status | Cognitive unreadiness | Society is not yet fully cognitively ready for AI. However, 3/2 can be considered ready. | MAIR score above average | Convergent |
| Readiness of nurses | ||||
| Readiness of society | ||||
| Readiness of students | ||||
| Nurses not being ready | ||||
| Society not being ready | ||||
| Students not being ready | ||||
| Increased efficiency | Improved quality of care | There are many benefits of using AI | The group's average MAIR score is above average | Convergent |
| Saving time | ||||
| Cost effectiveness | ||||
| Contribution to patient safety | ||||
| Reducing errors | ||||
| Facilitator | Contribution to workload reduction | Using AI can make things easier in many ways | The group's average MAIR score is above average | Complementary |
| Ease of teaching | ||||
| Ease of learning | ||||
| Ease of operation | ||||
| Anxiety | Decreased job satisfaction | Using AI causes anxiety in many ways | The mean AIA score of the participants included in the qualitative study was considerably higher than the other participants.
AIA score of all participants was higher than the lower limit. |
Complementary |
| Failure to understand patient emotions | ||||
| Increase in errors | ||||
| Decreased communication | ||||
| Increased cost | ||||
| Reduced information security | ||||
| Increasing unemployment | ||||
| Failure to ensure privacy | ||||
| Cognitive unreadiness | ||||
| Having unknowns about the future | ||||
| The possibility that they are better than nurses |
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