Attitudes and readiness of nursing students practising in surgical units towards artificial intelligence: a cross-sectional study

Abstract

Background

In surgical fields at the forefront of technological advancement with both virtual and physical artificial intelligence applications, nurses and nursing students should increase their positive attitudes toward artificial intelligence and demonstrate that they are ready to use it.

Methods

This study aims to examine the attitudes and readiness of nursing students practising in surgical units toward artificial intelligence. This cross-sectional study was conducted on 178 nursing students in the Faculty of Nursing at a university during the spring semester of the 2023-2024 academic year. “The Student Sociodemographic and Descriptive Characteristics Form”, “General Attitude towards Artificial Intelligence Scale”, and the “Medical Artificial Intelligence Readiness Scale for Medical Students” were used to collect study data. In this regard, students' positive and negative attitudes toward artificial intelligence, as well as their readiness in terms of cognition, ability, vision, and ethics related to artificial intelligence, were evaluated.

Results

In this study, the total mean score for positive attitudes on the General Attitude toward Artificial Intelligence Scale was 42.46±6.14, and for negative attitudes was 25.13±5.78. The total mean score on the Medical Artificial Intelligence Readiness Scale for Medical Students was 70.75±12, and the total mean scores of its factors were determined as 23.08±5.40 for cognition, 27.63±5.51 for ability, 9.68±2.07 for vision, and 10.35±2.40 for ethics.

Conclusions

Nursing students in surgical units showed generally positive attitudes toward artificial intelligence with moderate-to-high readiness, and greater positive attitudes were correlated with higher readiness. The study may help enhance nursing students’ awareness and understanding of artificial intelligence.

Background

As in many other fields, the use of Artificial Intelligence (AI) has been increasing in healthcare services [1], and this increase highlights the need for future healthcare professionals to develop competencies in this field. AI in healthcare is divided into two categories: virtual and physical. These two categories illustrate how AI is integrated into healthcare. The virtual category range from electronic patient record systems and decision-support algorithms to neural networks, image processing, natural language processing, speech recognition, statistical analysis, big data analytics, and predictive modelling. The physical category includes robots used in surgeries, smart prosthetics, and devices related to elderly care [2, 3].

It is anticipated that AI applications can improve the quality of healthcare services and reduce costs [1]. AI is used in medicine in many areas, such as collecting and interpreting health data, distinguishing medical images, making diagnoses, decision-making, providing supportive information in treatment and surgical procedures, and predicting the course of the disease [3–5]. AI is used in nursing in many areas, such as nursing diagnosis, care planning and prediction, monitoring patient hemodynamic data and outcomes, predicting fall risk and range of motion, and wound care management [6, 7]. AI applications in nursing have begun to influence nursing roles, workflows, and relationships with patients. In line with these evolving roles and interactions, it is stated that AI applications will assist and facilitate nursing practices, improve nursing education, and increase the quality of nursing care [1, 8]. Despite these potential benefits, ensuring patient safety and protecting patient privacy in AI applications in both medicine and nursing is of utmost importance [1].

It is stated that AI began with the invention of robots. With the introduction of robots into surgery, robotic surgical procedures also began [9]. Thus, notable progress has been made in the use of AI within the field of surgery. At the same time, surgery is one of the areas where AI applications can be best integrated [10]. In particular, the inadequacy of traditional surgery in meeting the requirements of modern precision surgery [11], the increase in surgical procedures and the number of patients, and the need to provide the best possible care have led to a demand for techniques such as AI [12]. AI applications are used from the preoperative period to the postoperative period [10]. A review of the literature shows that AI applications are used in reducing surgical trauma, increasing surgery duration and quality [11], improving discharge [12], predicting surgical site infections [13] and reducing the risk of falls [14]. In this regard, it is necessary for all healthcare professionals to adopt AI applications to improve and change traditional surgical care practices [15]. Thus, with the increasing role of AI in surgical care, changes in the competencies of healthcare professionals are expected. Surgeons and surgical nurses are key figures in the adoption of AI applications for surgical care. In order for surgeons and surgical nurses to interact with AI technologies, they must have a positive attitude toward AI, sufficient knowledge, and ultimately be prepared for AI applications [10]. Surgical nurses, who are an important member of the surgical team, have roles and responsibilities in the use of technologies such as AI [16].

Today, AI applications are rapidly increasing in both healthcare and surgical fields [10]. However, it is noted that AI applications are not widely supported by healthcare professionals. In order for AI to be used more effectively and frequently in healthcare services, it must be supported and adopted within the existing healthcare system [3]. In particular, students who will become healthcare professionals of the future have responsibilities regarding AI applications. This need becomes even more evident in surgical units, where AI applications are increasingly shaping clinical care. It is stated that students need to know the basics of AI and data science, mathematical concepts, and ethical and legal issues that will support medical care [4]. At the same time, nursing students must have at least a basic understanding of AI to interact with technology from the beginning of their higher education [17]. In this regard, nursing students are expected to embrace AI applications, exhibit a positive attitude, be ready, and use them.

There are studies in the literature that examine nursing students' attitudes, awareness, acceptance, and readiness toward artificial intelligence, focusing on their approaches and views as well [16, 18–21]. However, these studies mainly focus on general nursing education or clinical contexts rather than specific practice. In addition, a review of the national and international literature, no studies have been found that address nursing students' attitudes and readiness levels toward AI in the context of surgical clinical practice and specific to the field. It is believed that examining the attitudes and readiness of nursing students practising in surgical units AI will contribute to raising awareness among students, incorporating AI applications into surgical course content, and indirectly contribute to future integration of AI into surgical care. Gaining a clearer understanding of nursing students’ attitudes and readiness toward AI in surgical care will help inform curriculum development, support the development of future AI integrated training approaches, and guide institutional planning for the integration of AI into surgical care. This study aimed to examine the attitudes and readiness of nursing students practising in surgical units toward AI. This study fills an important gap by providing the first evidence focused specifically on surgical nursing education.

Research Question:

1. What are the attitudes of nursing students practising in surgical units toward AI?

2. What is the level of readiness of nursing students practising in surgical units toward AI?

3. What is the relationship between the attitudes toward AI and the readiness scale levels of nursing students practising in surgical units?

Methods

Design

This study is cross-sectional. This study was conducted in the spring semester of the 2023-2024 academic year at a university's Faculty of Nursing.

Participants

The population of this study consisted of 195 second-year nursing students enrolled in the Nursing Faculty of a university during the spring semester of the 2023-2024 academic year who were doing clinical practice in surgical units as part of the Diseases and Nursing Care I Course. The sample of this study consisted of 178 second-year nursing students enrolled in the Nursing Faculty of a University during the spring semester of the 2023-2024 academic year, who were continuing their clinical practice in surgical units and volunteered to participate in the study. Four students who did not continue their clinical practice and 13 students who refused to participate in the study were not included in the study (Fig. 1). The second-year nursing students at the undergraduate level in this study are receiving basic theoretical training in surgical nursing and participating in a clinical practice rotation in surgical units. These students are not specialized; they are taking courses in surgical nursing as general nursing students.

Fig. 1.

Population, sample and data collection

To determine whether the sample size used in the study was statistically adequate, both a priori and post hoc power analyses based on correlation analysis were performed using G*Power 3.1 software. In the a priori analysis, based on the literature, assuming a moderate effect size (r = 0.30), α = 0.05 (two-tailed), and 80% power (1−β = 0.80), a minimum of 84 participants was determined to be necessary. The 178 participants included in this study significantly exceeded the sample size required for the expected effect size. In the post hoc power analysis, the observed correlation coefficient (r = 0.79) between the study's main variables, GAASI and MAIRS-MS, was used. With this observed effect size, calculations performed at the α = 0.05 level showed that the study's statistical power was above 99% (1−β > 0.99). This finding indicates that the study has high sensitivity in terms of correlational analyses and that the probability of missing meaningful relationships is quite low.

Data collection tools

A data collection form was used to collect study data. The data collection form included three data collection tools. These tools are described below:

• Student Sociodemographic and Descriptive Characteristics Form: This form was used to determine the sociodemographic and descriptive characteristics of nursing students. Researchers developed the form through a literature review [18, 21–23]. The form includes a total of 15 questions, including sociodemographic characteristics, internet and technology use, AI use, an open-ended question and 14 multiple-choice questions related to AI in nursing.

• General Attitude towards Artificial Intelligence Scale (GAASI): GAASI was developed by Schepman and Rodway to assess attitudes towards AI [24] and adapted into Turkish by Kaya et al. The scale consists of two subscales: positive and negative attitudes. Items 1–12 on the scale represent positive attitudes, while items 13–20 represent negative attitudes. Thus, the scale consists of 20 items. The scale is a 5-point Likert-type scale, and participants are asked to select the statement they find appropriate from the options 1=Strongly Disagree, 2=Disagree, 3=Undecided, 4=Agree, 5=Strongly Agree. Items expressing negative attitudes are reverse-scored. The Cronbach's alpha value for the scale is 0.82 for positive attitudes and 0.84 for negative attitudes [25]. In this study, the Cronbach's alpha value is 0.845 for positive attitudes and 0.865 for negative attitudes.

• Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS): MAIRS-MS was developed by Karaca et al. to assess medical students' perceived readiness for AI and its applications in medicine, and its validity and reliability have also been established. The scale consists of four facto: cognition, ability, vision, and ethics and a total of 22 items. The cognition factor measures participants' fundamental concepts, logic, and data science knowledge related to medical AI; the ability factor measures their ability to select the appropriate AI application, integrate it with professional knowledge, and explain it to the patient. The vision factor includes the ability to evaluate the strengths and weaknesses of AI, express its limitations, and anticipate potential opportunities and threats. The ethics factor evaluates participants' compliance with legal and ethical principles in the use of artificial intelligence. The scale is a 5-point Likert-type scale, and participants are asked to select the statement they find appropriate from the options 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree, and 5=Strongly Agree. The sum of these scores yields scores ranging from. 4 to 40 for the cognition factor, 8 to 40 for the ability factor, 3 to 15 for the vision factor, and 3 to 15 for the ethics factor. The total score from the scale ranges from 22 to 110. As the score obtained from the scale increases, readiness increases. The Cronbach's alpha value for the entire scale is 0.877. The Cronbach's alpha values for the factors of the scale are 0.830 for the cognition, 0.770 for the ability factor, 0.723 for the vision factor, and 0.632 for the ethics factor [4]. In this study, the Cronbach's alpha value for the entire scale is 0.932. The Cronbach's alpha values for the scale's factors are 0.890 for the cognition, 0.892 for the ability, 0.781 for the vision factor, and 0.841 for the ethics factor.

Data collection process

The researchers first explained the purpose of the study to nursing students who agreed to participate in the study and obtained written informed consent from the students. Then, the data collection form was distributed to the students, and they were asked to fill it out. It took the students approximately 10–15 minutes to fill out the data collection form. While collecting data, care was taken not to disrupt nursing students' practices.

Analysing the data

The study data were analysed using the Statistical Package for the Social Sciences (SPSS) for Windows 25.0 IBM program. Descriptive statistics such as mean, median, standard deviation, percentage, and frequency were used in the analysis of descriptive data. The normality assumption of quantitative data was checked in groups using the Shapiro-Wilk W test and Skewness/Kurtosis values. For variables showing a normal distribution, the independent samples t-test and one-way analysis of variance (ANOVA) were used, while for variables not showing a normal distribution, the Mann-Whitney U test and Kruskal-Wallis test were used. Pearson correlation analysis was used to determine the relationship between two variables. All results with a p-value <0.05 were considered statistically significant.

Particular attention was paid to ensuring that questionnaires were completed in full during the data collection process, and no missing data was identified in this regard. Therefore, there was no situation that required the exclusion of questionnaires containing missing data from the study.

Study ethics

Before collecting study data, ethical approval was obtained from the Scientific Research and Publication Ethics Committee (Decision Date: January 30, 2024, Protocol No: 2313), institutional approval was obtained from the faculty administration, and written informed consent was obtained from nursing students. Permission was also obtained from the authors via email to use the scales in the study. The study was conducted by the principles of the Helsinki Declaration.

Results

Sociodemographic and descriptive characteristics

The mean age of the students was 21.58 ±11.32 (Min: 19, Max:33) years, and %78.7 of them were female. A total of %23.6 had practised in an orthopaedics and traumatology department. Internet use ranged from three to six hours daily for %64.6 of the students, while %67.4 reported a medium level of technological competence. AI applications were used by %85.4 of the participants, and %66.3 stated that they had a medium level of understanding of AI. In academic tasks, %66.3 used AI for assignments and %77.5 used it for language translation. Only %21.3 reported knowledge of AI in surgical nursing, and %73 considered robotic surgery an AI application. Overall, %90.4 believed AI would improve nursing practice, and %79.8 stated that AI would positively influence their professional careers (Table 1).

Table 1.

Sociodemographic and descriptive characteristics

Sociodemographic and descriptive characteristics	Number (n)	Percentage (%)
Age: 21.58 ±11.32 (Min: 19, Max:33)
Gender
Female	140	78.7
Male	38	21.3
The Surgical Departments where practice is performed
Orthopaedics and Traumatology	42	23.6
Neurosurgery	34	19.1
Cardiovascular Surgery	29	16.3
General Surgery	27	15.2
Emergency	22	12.4
Organ transplant	12	6.7
Plastic, Reconstructive and Aesthetic Surgery	6	3.4
Thoracic Surgery	6	3.4
How many hours do you use the internet per day?
Less than 3 hours	29	16.3
3–6 hours	115	64.6
More than 6 hours	34	19.1
How do you find your technological competence?
Low	13	7.3
Medium	120	67.4
High	45	25.3
Do you use AI applications?
Yes (Gemini, ChatGPT, Canva)	152	85.4
No	26	14.6
What is your understanding of AI applications?
Low	41	23
Medium	118	66.3
High	19	10.7
How do developments in AI affect you?
Exciting	65	36.5
Fear	30	16.9
Neutral	83	46.6
Do you use AI to prepare assignments?
Yes	118	66.3
No	60	33.7
Do you use AI in language translation?
Yes	138	77.5
No	40	22.5
Do you have any information about AI applications in nursing?
Yes	38	21.3
No	140	78.7
Do you have any information about AI applications in surgical nursing?
Yes	24	13.5
No	154	86.5
Do you think robotic surgery is an application of AI?
Yes	131	73.6
No	47	26.4
Do you think AI will improve nursing?
Yes	161	90.4
No	17	9.6
Do you think AI will positively impact your career?
Yes	142	79.8
No	36	20.2
Total	178	100

General attitude towards artificial intelligence scale total mean score and related factors

In this study, the GAASI positive attitude total mean score was found to be 42.46±6.14, and the GAASI negative attitude total mean score was found to be 25.13±5.78.

m mean scores than those who understood them at a low level (KW: 12.113, p: 0.002), and students who expressed exciting about developments in the field of AI had higher mean positive attitude scores toward AI compared to those who expressed fear or neutral toward these developments (KW: 32.889, p: 0.000). Those who use AI applications (U: −3.807, p: 0.000), those who benefit from AI while preparing their assignments (U: −3.524, p: 0.000), those who benefit from AI in language translation (U: −3.612, p: 0.000), believe that AI applications will improve nursing (U: −2.093, p: 0.036), and believe that AI will positively affect their career (U: −2.093, p: 0.036) had higher mean positive attitude scores toward AI. According to the Bonferroni correction, students who stated that they were afraid of developments in the field of AI had a lower average total negative attitude score (K: 22.927, p: 0.000) than those who stated that they were excited and neutral (Table 2).

Table 2.

Distribution of general attitude towards artificial intelligence scale and medical artificial intelligence readiness scale for medical students total mean scores according to sociodemographic and descriptive characteristics

Sociodemographic and descriptive characteristics	Total mean scores of GAASI positive attitude Median±SD Test Value	Total mean scores of GAASI negative attitude Mean±SD Test Value	Total mean scores of MAIRS-MS Median±SD Test Value	Total Mean score of the cognition factor of the MAIRS-MS Mean±SD Test Value	Total mean score of the ability factor of the MAIRS-MS Median±SD Test Value	Total mean score of the vision factor of the MAIRS-MS Median±SD Test Value	Total mean score of the ethics factor of the MAIRS-MS Median±SD Test Value
Gender
Female	42.00±5.78	25.43±5.68	73.00±12.59	22.81±5.13	29.00±5.61	10.00±2.16	11.00±2.30
Male	43.50±7.07 U: −1.649 p: 0.099	24.05±6.11 t: −1.670 p: 0.095	74.00±13.78 U: −0.856 p: 0.392	24.05±6.26 t:−0.856 p:0.392	28.03±5.24 U: −0.054 p: 0.957	28.03±1.73 U: −0.339 p: 0.735	10.74±2.73 U: −1.003 p: 0.316
How many hours do you use the internet per day?
Less than 3 hours	42.00±5.93	23.66±5.84	73.00±10.95	23.69±4.85	29.00±5.01	9.00±1.45	10.00±2.17
3–6 hours	43.00±5.88	25.16±5.58	73.00±12.55	22.97±5.24	28.00±5.42	10.00±2.02	11.00±2.34
More than 6 hours	45.50±7.06 KW:3.658 p:0.161	26.32±6.27 F:4.702 p:0.095	73.00±15.34 KW:0.744 p:0.689	22.94±6.42 F:0.240 p:0.887	30.00±6.23 KW:2.530 p:0.282	10.00±2.62 KW:1.898 p:0.387	12.00±2.76 KW:1.934 p:0.380
How do you find your technological competence?
Low	39.00±4.84	23.85±5.55	69.00±10.83	24.15±4.37	25.00±5.64	9.00±1.55	9.00±2.16
Medium	43.00±5.84	24.98±5.59	72.00±13.07	22.41±5.32	29.00±5.71	10.00±2.06	10.00±2.49
High	43.00±6.90 KW:7.358 p:0.025	25.93±6.35 F:1.820 p:0.402	75.00±12.13 KW:6.298 p:0.043	24.56±5.63 F:6.212 p:0.045	30.00±4.67 KW:4.985 p:0.083	10.00±2.25 KW:5.250 p:0.072	11.00±2.14 KW:2.002 p:0.368
Do you use AI applications?
Yes (Gemini, ChatGPT, Canva)	43.50±5.82	25.10±5.84	73.00±12.50	23.39±5.33	29.00±5.30	10.00±2.04	11.00±2.43
No	38.00±6.66 U:−3.807 p:0.000	25.35±5.50 t:−0.458 p:0.647	68.00±13.88 U: −1.972 p:0.049	21.27±5.53 t:−1.561 p:0.119	26.50±6.30 U: −1.937 p:0.053	9.00±2.26 U:−0.556 p:0.578	10.00±2.04 U: −2.044 p:0.041
What is your understanding of AI applications?
Low	41.00±5.87	24.66±5.30	66.00±12.43	20.34±5.18	26.00±5.92	10.00±2.25	10.00±2.11
Medium	44.00±5.91	25.77±5.84	75.00±12.76	24.14±5.12	30.00±5.35	10.00±2.09	11.00±2.51
High	41.00±6.77 KW:12.113 p:0.002	22.21±5.65 F:5.036 p:0.081	73.00±10.78 KW:13.715 p:0.001	22.42±5.63 F:16.193 p:0.000	29.00±4.47 KW:11.072 p:0.004	10.00±1.52 KW:0.436 p:0.804	11.00±2.04 KW:4.880 p:0.087
How do development s in AI affect you?
Exciting	45.00±6.43	26.57±5.63	76.00±13.20	24.12±5.76	29.00±5.32	10.00±1.96	11.00±2.59
Fear	40.00±4.76	20.73±5.19	71.50±12.19	22.17±4.87	26.50±6.04	10.00±1.81	11.00±1.83
Neutral	41.00±5.15 KW:32.889 p:0.000	25.60±5.38 F:22.927 p:0.000	71.00±12.65 KW:4.896 p:0.086	22.59±5.22 F:3.349 p:0.187	29.00±5.38 KW:5.455 p:0.65	10.00±2.26 KW:0.656 p:0.720	10.00±2.44 KW:0.791 p:0.673
Do you use AI to prepare assignments?
Yes	44.00±6.28	25.23±6.35	73.00±13.78	23.23±5.68	29.00±5.79	10.00±2.10	11.00±2.59
No	41.00±5.35 U:−3.524 p:0.000	24.95±4.50 t:−0.131 p:0.896	72.50±10.96 U: −0.850 p:0.395	22.78±4.82 t:−0.275 p:0.395	28.00±4.95 U:−0.880 p:0.379	10.00±1.99 U:−0.818 p:0.413	10.50±1.99 U: −0.212 p:0.832
Do you use AI in language translation?
Yes	44.00±5.66	25.31±5.96	73.00±12.79	23.59±5.26	29.00±5.44	10.00±2.10	11.00±2.47
No	39.50±6.99 U:−3.612 p:0.000	24.53±5.13 t:−0.606 p:0.545	71.50±12.56 U: −1.951 p:0.051	21.30±5.56 t:−2.072 p:0.038	27.50±5.68 U: −1.490 p:0.136	10.00±1.98 U: −0.012 p:0.990	10.00±2.10 U: −1.763 p:0.780
Do you have any information about AI applications in nursing?
Yes	44.50±7.69	25.61±6.89	76.00±14.34	25.95±5.92	30.00±6.04	10.00±2.38	10.50±2.54
No	42.00±5.62 U: −1.848 P:0.065	25.01±5.46 t:−0.288 p:0.773	72.00±12.20 U: −2.584 p: 0.010	22.30±4.99 t:−3.556 p:0.000	29.00±5.35 U: −1.443 p:0.149	10.00±1.98 U:−1.259 p:0.208	11.00±2.37 U:-.0122 p:0.903
Do you have any information about AI applications in surgical nursing?
Yes	41.50±8.67	26.08±7.14	73.00±12.56	25.21±6.58	29.00±5.42	10.00±2.28	10.00±2.31
No	43.00±5.67 U:−0.297 p:0.767	24.99±5.55 t:−0.425 p:0.671	73.00±12.84 U:−1.031 p:0.302	22.75±5.14 t:−1.629 p:0.103	29.00±5.53 U:−0.698 p:0.485	10.00±2.04 U:−0.743 p:0.457	11.00±2.42 U:−0.479 p:0.632
Do you think robotic surgery is an application of AI?
Yes	43.00±6.34	25.28±5.71	75.00±12.55	23.85±5.25	29.00±5.44	10.00±2.08	11.00±2.32
No	43.00±5.55 U: −0.481 p:0.630	24.72±6.01 t:−0.235 p:0.814	66.00±12.34 U: −3.553 p: 0.000	20.94±5.28 t: −2.944 p: 0.003	28.00±5.53 U: −2.262 p: 0.024	9.00±1.93 U: −2.817 p:0.005	10.00±2.35 U:−3.011 p:0.003
Do you think AI will improve nursing?
Yes	43.00±5.92	25.27±5.85	73.00±12.66	23.42±5.43	29.00±5.42	10.00±2.02	11.00±2.38
No	39.00±7.35 U:−2.093 p:0.036	23.82±5.01 t:−0.746 p:0.455	69.00±13.57 U:−1.949 p:0.051	19.82±3.87 t:−2.816 p:0.005	27.00±6.04 U: −0.888 p: 0.375	9.00±2.59 U: −0.475 p: 0.635	10.00±2.41 U: −2.000 p:0.046
Do you think AI will positively impact your career?
Yes	44.00±5.58	25.74±5.82	73.00±11.90	23.68±5.29	29.00±4.99	10.00±1.91	11.00±2.37
No	37.00±6.12 U: −4.902 p:0.000	22.75±5.02 t:−2.522 p:0.012	70.00±14.76 U:−2.562 p:0.010	20.72±5.25 t:−2.805 p:0.005	27.00±6.79 U:−2.369 p:0.018	9.50±2.62 U:−0.729 p:0.466	10.00±2.32 U:−2.484 p:0.013

SS: Standard deviation; t: Independent Samples t-test; F: ANOVA; KW: Kruskal-Wallis test; U: Mann-Whitney U test (z value); p < 0.05 was considered statistically significant)

Medical artificial intelligence readiness scale for medical student total mean score and related factors

In this study, the total mean score of the MAIRS-MS was 70.75±12.85. The total mean scores of MAIRS-MS factors were as follows: cognition 23.08±5.40, ability 27.63±5.51, vision 9.68±2.07, and ethics 10.35±2.40."

According to the Bonferroni correction, students who perceived themselves as having high technological competence had higher MAIRS-MS total mean scores (KW: 6.298, p: 0.043) and cognition factor total mean scores (KW: 6.212, p: 0.045) than those who perceived themselves as having low competence. Again, according to the Bonferroni correction, students who understood AI applications at a medium level had higher MAIRS-MS total mean score (KW: 13.715, p: 0.00), cognition factor (KW: 16.193, p: 0.000), and ability factor total mean scores (KW: 11.072, p: 0.004) were found to be higher. The total mean score of students who used AI applications in MAIRS-MS (U: 1497.5, p: 0.049), the ability factor (U: −1.937, p: 0.053), and the ethics factor (U: −2.044, p: 0.041) were found to be higher. The total score average of the cognitive factor was found to be higher (U: −2.072, p: 0.038) among students who used AI in language translation. Students who were knowledgeable about AI applications in nursing had higher MAIRS-MS total mean score (U: −2.584, p: 0.010) and cognition factor total mean score (U: −3.556, p: 0.000). Students who evaluated robotic surgery as an AI application had higher MAIRS-MS total mean score (U: −3.553, p: 0.000), cognition factor (U: −2.944, p: 0.003), ability factor (U: −2.262, p: 0.024), vision factor (U: −2.817, p: 0.005), and ethics factor total mean scores (U: −3.011, p: 0.003) were found to be higher. The MAIRS-MS total mean score of students who believe that AI will positively affect their careers (U: −2.562, p: 0.010), cognition factor (U: −2.805, p: 0.005), ability factor (U: −2.805, p: 0.005), and ethics factor total mean scores (U: −2.484, p: 0.013) were found to be higher (Table 2).

The relationship between the mean scores of the general attitude towards artificial intelligence scale and the medical artificial intelligence readiness scale for medical students

In this study, a statistically significant positive correlation was found between the total mean score of the positive attitude of the GAASI and the overall total mean score of the MAIRS-MS (r = 0.790, p < 0.001). Additionally, positive correlations were observed between the GAASI positive attitude total mean score and each of the MAIRS-MS factors: the cognition factor (r = 0.291, p < 0.001), ability factor (r = 0.353, p < 0.001), vision factor (r = 0.248, p = 0.001), and ethics factor (r = 0.231, p = 0.002). A statistically significant positive correlation was found between the total mean score for negative attitude toward GAASI and the total mean score for MAIRS-MS (r: 0.758, p: 0.000) and the total mean score for the ability factor (r: 0.353, p: 0.000) (Table 3).

Table 3.

Correlation between the total mean scores of the general attitude towards artificial intelligence scale and the medical artificial intelligence readiness scale for medical students

Variables	Total mean Scores of MAIRS-MS	Total Mean Score of the Cognition Factor of the MAIRS-MS	Total Mean Score of the Ability Factor of the MAIRS-MS	Total Mean Score of the Vision Factor of the MAIRS-MS	Total Mean Score of the Ethics Factor of the MAIRS-MS
Total Mean Scores of GAASI Positive Attitude	r:0.790 p:0.000	r:0.291 p:0.000	r:0.353 p:0.000	r:0.248 p:0.001	r:0.231 p:0.002
Total Mean Scores of GAASI Negative Attitude	*r:0.758 p:0.000	r:0.030 p:0.692	r:0.190 p:0.011	r:−0.019 p:0.802	r:−0.016 p:0.827

r: Pearson correlation coefficient; *r: Spearman correlation coefficient significant at p<0.05 level)

Discussion

This study examined nursing students' attitudes toward artificial intelligence, their readiness, and related factors in surgical units. As in the healthcare field in general [26], the increasing prevalence of AI applications in surgical units [27] highlights the importance of assessing the attitudes and readiness of nursing students working in these units toward AI to enhance their professional education.

Nursing students' general attitude towards artificial intelligence

This study found that nursing students practising in surgical units have a generally positive attitude towards artificial intelligence. Similarly, the literature indicates that nursing students have a positive attitude toward AI [22, 28, 29]. This positive attitude indicates that nursing students have significant intentions to use AI, which may facilitate the adoption and implementation of AI applications [29, 30]. To maintain and enhance this positive attitude among students practising in surgical units, AI applications should be incorporated into the theoretical content of surgical nursing courses, followed by opportunities for students to observe and understand AI applications such as robotic surgery and decision support systems in clinical settings.

In this study, students who have a medium understanding of AI applications, are excited about developments in the field of intelligence, use AI applications in assignments or language translation, and believe that AI applications will have a positive impact on their professional careers have been found to have more positive attitudes toward artificial intelligence. The literature also indicates that students with experience in technology and AI use have higher positive attitudes toward AI [28, 29]. Students who are tech-savvy, open to innovation, and use AI in their personal and academic lives are found to have more positive attitudes toward AI. In this regard, it is recommended that students practising in surgical units be introduced to AI applications as field-specific applications.

Nursing students' readiness for medical artificial intelligence

In this study, the readiness of nursing students practising in surgical units for AI generally ranges from moderate to high. The literature indicates that midwifery and nursing students' readiness for AI is moderate [31]. Similarly, it is stated that nursing students are generally ready for the use of AI, and that fourth-year students in particular are more ready for AI integration. This situation has been explained by authors as fourth-year students gaining more clinical experience and becoming more familiar with the use of artificial intelligence in clinical applications [32]. Repeating this study when students reach fourth-year would be useful for assessing progress in attitudes and readiness toward artificial intelligence. It can be said that nursing students are open to and interested in technological developments such as AI. In this regard, it is recommended that courses such as AI nursing be added to the educational content to maintain and increase nursing students' readiness for AI, that leadership self-efficacy characteristics be increased [33, 34], and that they be enabled to play an active role in surgical units.

In this study, it was determined that students who considered themselves to have high technological competence relative to low competence, understood AI applications at a medium level relative to a low level, knew artificial intelligence applications in nursing, evaluated robotic surgery as an AI application, and believed that AI would have a positive impact on their careers had higher cognition readiness. The cognitive domain indicates a basic understanding and knowledge of the field of artificial intelligence [35]. The scale also indicates that the cognition factor evaluates cognitive readiness in terms of medical AI applications, the logic of AI applications, and terminological knowledge related to data science [4]. In this context, it is thought that nursing students' proficiency in technology use and awareness of surgery-specific applications, in addition to their theoretical knowledge, support their cognitive readiness for AI.

In this study, it was determined that students who understand AI applications at a medium level, use AI applications, consider robotic surgery to be an AI application, and believe that AI will have a positive impact on their careers have a higher level of readiness in terms of ability readiness. The ability domain demonstrates the student's ability to select an appropriate AI application, use it, and explain it to patients [36]. The scale also indicates that the ability factor evaluates competence in selecting an appropriate medical AI application, combining it with professional knowledge to use it appropriately, and explaining it to the patient [4]. In this context, it is thought that students' awareness of AI applications in clinical practice, in addition to their knowledge of AI applications, supports their AI readiness in terms of ability.

In this study, it was observed that students who use AI applications, view robotic surgery as an example of AI, and believe that AI will positively impact their professional future demonstrated higher ethical preparedness. The ethical factor of the scale assesses compliance with legal and ethical rules regarding the use of AI in healthcare [4]. Therefore, students' direct interaction with AI and increased awareness of these applications may contribute to the development of a more conscious approach to ethical issues. Furthermore, it was found that students who considered robotic surgery as AI scored higher on the vision factor. The vision factor encompasses the ability to evaluate the strengths and weaknesses of AI, anticipate potential risks and opportunities, and develop new ideas [4]. Students who closely follow technological developments appear to be better prepared in this area. This finding shows that introducing new technologies to students in surgical nursing education, both theoretically and practically, contributes significantly to the development of their vision regarding AI.

Nursing students' general attitudes and readiness toward medical artificial intelligence

In this study, a positive relationship was found between nursing students' general attitudes toward AI and their readiness. This finding shows that as students' general positive attitudes toward AI increase, their readiness toward AI also increases. The literature also indicates that positive attitudes toward AI are related to readiness [32, 37]. In this regard, it is recommended that topics related to the use of AI be integrated into theoretical and practical course content to support nursing students' attitudes and readiness toward AI at the cognition, ability, vision, and ethics levels.

Limitations

This study was conducted at a specific university's nursing faculty, which limits the generalizability of the results. Furthermore, the fact that the data was collected within a specific time frame indicates that attitudes toward AI may change over time.

In this study, data were collected on a voluntary basis, which may introduce bias in the selection of participants. Furthermore, the self-report nature of the scales carries the risk of social desirability bias, which may lead students to provide more acceptable responses than they actually think. This situation may partially limit the generalizability of the results obtained.

Conclusions

In this study, it can be said that nursing students practising in surgical units have generally positive attitudes toward AI and that their readiness for AI is between medium and high levels. At the same time, this study found that as positive attitudes toward AI increase, so does readiness. In this regard, it is recommended that content related to AI technologies be integrated into undergraduate education, students' technology use skills be developed, and surgical-specific AI applications be introduced to maintain and increase positive attitudes and readiness toward AI. It is believed that these recommendations will contribute to the individual and professional development of nursing students and may improve the quality of surgical patient care and patient safety.

Abbreviations

AI

Artificial Intelligence

GAASI

General Attitude towards Artificial Intelligence Scale

MAIRS-MS

Medical Artificial Intelligence Readiness Scale for Medical Students

SPSS

Statistical Package for the Social Sciences

Authors’ contributions

Study design: NCD Data collection: NCD, RHK Data analysis: NCD, RHK Study supervision: NCD Manuscript writing: NCD, RHK Critical revisions for important intellectual content: NCD

Funding

This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the 2209-A Undergraduate Student Research Projects Support Program. (Project number: 1919B012326723).

Data availability

The data that support the findings of this study are available from the authors upon reasonable request.

Declarations

Ethics approval and consent to participate

All participants provided their written informed consent before participating in the study. The study was conducted by the Helsinki Declaration and was approved by the Scientific Research and Publication Ethics Committees of Ege University (Decision Date: January 30, 2024, Protocol No: 2313).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.