Abstract
Introduction
As artificial intelligence (AI) becomes more common in healthcare, nursing students need to be both mentally and emotionally ready to use it. But feeling anxious about technology might hold them back. This study looked at whether there is a link between AI Readiness and technology anxiety among nursing students.
Methods
We carried out a descriptive-correlational study with 297 nursing students at Qom University of Medical Sciences during the 2024–2025 academic year. We used census sampling, meaning we invited all eligible students to take part. Data were collected using a demographic form, the Abbreviated Technology Anxiety Scale (ATAS), and the Medical AI Readiness Scale for Medical Students (MAIRS-MS). Data were analyzed using descriptive statistics, Pearson correlation, independent t-tests, and multiple linear regression to identify predictors of AI Readiness.
Results
The mean scores were 3.76 ± 0.54 for technology anxiety and 3.68 ± 0.61 for AI Readiness, indicating moderate-to-high levels. A strong inverse correlation was found between AI Readiness and technology anxiety (r = − 0.648, p < 0.001). Multiple linear regression showed that AI Readiness was a significant negative predictor of technology anxiety (B = − 0.32, p < 0.001), explaining 42% of the variance (R² = 0.420). No significant differences were observed based on gender, age, or marital status. Students in higher academic years reported lower technology anxiety (r = − 0.121, p = 0.026), and master’s students demonstrated significantly higher AI Readiness compared to bachelor’s students (p = 0.011).
Conclusion
These findings show that nursing students who feel more prepared for AI tend to be less anxious about technology. Adding AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future. Further longitudinal and interventional studies are needed to better understand causal relationships and to identify effective educational strategies.
Keywords: AI Readiness, Technology anxiety, Nursing students, AI in nursing education, Digital literacy
Background
Nowadays, with the significant improvements of digital technology, the use of Artificial Intelligence (AI) in the fields of medicine and medical research has been drastically increased [1, 2]. One of the most significant applications of AI in the last few years has been within the healthcare industry, where it has assisted healthcare staff in various fields by improving procedure precision, optimizing nursing programs, and reducing the administrative responsibilities, and has ultimately led to enhanced efficiency along with better patient care outcomes [3]. However, the effective utilization of AI tools in different fields, especially healthcare, requires individuals to be ready cognitively and attitudinally [4]. This readiness is a key concept which encompasses various dimensions and will be a fundamental part of this study’s discussion.
In concept, AI Readiness consists of knowledge, skills, and positive attitudes required to effectively adopt AI technology as an intelligent and reliable assistant [5]. While AI systems themselves offer capabilities such as data interpretation, clinical decision support, and ethical reasoning [6, 7], the successful integration of these technologies into practice depends primarily on human readiness — that is, the cognitive and emotional preparedness of healthcare providers to trust, understand, and use AI appropriately [8]. Therefore, this study focuses on AI Readiness as a human trait, not a technological feature. Previous studies have reported a significant negative correlation between AI Readiness and AI anxiety among nursing students, with correlation coefficients ranging from r = − 0.64 in a Chinese sample [9] to r = − 0.58 in a Jordanian sample [10]. However, most existing research has focused on general AI anxiety rather than technology-specific anxiety, and few studies have examined this relationship in the Iranian context. Furthermore, no study to date has explored these variables among nursing students in Qom, Iran, where digital infrastructure and AI education may differ from other settings.
One of the more significant challenges of achieving this readiness is Technology Anxiety that referred to as the apprehension, fear or discomfort that people experience when using or encountering new or complex technologies. This psychological obstacle is caused by the lack of sufficient knowledge, fear of making a mistake, or being concerned about the possible negative consequences of technology [2]. This anxiety may have irreversible and disruptive impacts in field of technology education and application, which might eventually lead to impaired performance and resistance towards adopting new technologies [11–13]. This overall interaction causes reduced self-esteem in individuals, especially nurses, regarding the use of newer technologies, which in turn might result in distrust in decisions made by AI algorithms [14].
Previous studies have examined technology anxiety in medical or nursing students, but few have linked it with AI Readiness. Although similar associations have been reported in other populations, such as Chinese nursing students (Fan et al., 2025), the present study is among the first to examine this relationship specifically in the Iranian context and to focus on technology anxiety rather than general AI anxiety. As stated earlier, modern nursing education is being implemented increasingly through digital tools, and modern AI-based learning platforms. However, the successful adoption of these technologies in education significantly relies on psychological readiness of students, students and nurses who have higher levels of technology anxiety are more likely to be hindered in the use of AI-based tools [15, 16]. As mentioned, high levels of technology anxiety might be one of the obstacles that reduce the interaction of nurses with AI, which can lead to a gap in theoretical and practical education and also prevent the full integration of AI into the healthcare practices, meaning lower AI Readiness of students and nurses. According to the importance of AI and technology anxiety and lack of study about this challenge, present study is aiming to figure out the association between AI Readiness and Technology Anxiety to provides a better insight towards developing strategies for training skilled and efficient nurses.
Method
Design and sample
The statistical population comprised all 297 nursing students at the Faculty of Nursing, Qom University of Medical Sciences, during the 2024–2025 academic year. A census sampling method was used, and all 297 students were invited to participate. Of these, 297 completed the survey in full (response rate = 100%), as data collection was conducted during mandatory class sessions. No incomplete questionnaires were excluded.
Data collection tools
Data were collected using a three-section questionnaire:
Demographic Checklist: This section – prepared by the authors – was utilized to gather data regarding the age, gender, marital status, year of education and level of education of the participants.
Abbreviated Technology Anxiety Scale (ATAS): This scale, which was developed and abbreviated by Wilson et al. (2023), was used to measure the level of technology anxiety. The Persian version of this scale was validated by Razavi et al. (2024), and involves 11 items which are rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The total score ranges from 11 to 55 points, with the higher scores being indicative of greater technology anxiety. This scale has shown good validity and its reliability has been confirmed in the original study with a Cronbach’s alpha of 0.96 [17] and in the Persian context with a Cronbach’s alpha of 0.88 [18].
Medical AI Readiness Scale for Medical Students (MAIRS-MS): AI Readiness was evaluated using the MAIRS-MS scale, developed by Karaca et al. (2021). The Persian version of this scale which was validated by Ghalibaf et al. (2023), includes 22 items and four subscales (cognition, ability, vision, and ethics). Responses are rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), with the minimum and maximum scores being 22 (low AI Readiness) and 110 (high AI Readiness) respectively. The scale has demonstrated great reliability, with the original developers reporting a Cronbach’s alpha of 0.877 [5] while the Persian version reports a score of 0.944 [19].
In the present sample, the Cronbach’s alpha was 0.89 for the ATAS and 0.93 for the MAIRS-MS, indicating excellent internal consistency.
Data collection process
Data collection was conducted in-person over a two-month period during the 2024–2025 academic year. A comprehensive questionnaire comprising demographic information, the ATAS, and the MAIRS-MS was administered to nursing students at Qom University. The anonymity and confidentiality of participants’ information were maintained. Before continuing to complete the questionnaire, participants signed their informed consent on the first page of the form.
Inclusion and exclusion criteria
The inclusion criteria included being a nursing student of Qom University of Medical Sciences, currently studying, and over 18 years of age. The exclusion criteria also include unwillingness to participate in the study and submitting an incomplete questionnaire.
Ethical approval
“This study was conducted in accordance with the Declaration of Helsinki (2024 revision). The protocol was reviewed and approved by the Ethics Committee of Qom University of Medical Sciences (Code: IR.MUQ.REC.1404.091). Informed consent was obtained from all participants, detailing the study objectives, procedures, voluntary participation, and data confidentiality.
Statistical analysis
Data analysis was performed using SPSS version 24. Descriptive statistics (frequencies, percentages, means, standard deviations) were used to summarize demographic characteristics and scale scores. To facilitate interpretation and comparison across scales, total scores were converted to item-mean scores by dividing the total score by the number of items (11 for ATAS, 22 for MAIRS-MS). Thus, all scores are reported on a 5-point Likert scale [1–5]. The relationship between technology anxiety and AI Readiness was examined using Pearson correlation coefficient. Independent samples t-tests were used to compare mean scores between two groups (e.g., gender, marital status, educational level). One-way ANOVA was used for comparisons across more than two groups (e.g., academic year, age groups). Multiple linear regression was performed to identify predictors of AI Readiness, with demographic variables and technology anxiety score entered as independent variables.
Prior to performing multiple linear regression, all assumptions were assessed
Normality of residuals was confirmed using the Shapiro–Wilk test (p > 0.05) and inspection of Q-Q plots.
Homoscedasticity was verified using scatter plots of standardized residuals versus predicted values.
Linearity was assessed using partial regression plots.
Multicollinearity was evaluated using the Variance Inflation Factor (VIF). All VIF values were below 2.0 (ranging from 1.02 to 1.45), indicating no significant multicollinearity among predictors.
Independence of residuals was confirmed using the Durbin–Watson statistic (d = 1.94, close to 2). For all analyses, p < 0.05 was considered statistically significant.
Results
The normality of continuous variables was assessed using the Shapiro–Wilk test and inspection of skewness and kurtosis values. All variables showed acceptable normality (skewness < |2|, kurtosis < |7|), allowing the use of parametric tests.
A total of 297 nursing students participated in this study. More than half of the participants were male (n = 167, 56.2%), and most were single (n = 228, 76.8%). Regarding academic year, the largest proportion of students were in their second year of study (n = 98, 33.0%). With respect to age, the majority of participants were between 22 and 25 years old (n = 134, 45.1%) Table 1.
Table 1.
Demographic characteristics of participants (N = 297)
| Variable | Category | n (%) |
|---|---|---|
| Gender | Male | 167 (56.2) |
| Female | 130 (43.8) | |
| Marital status | Single | 228 (76.8) |
| Married | 69 (23.2) | |
| Academic year | First year | 40 (13.5) |
| Second year | 98 (33.0) | |
| Third year | 80 (26.9) | |
| Fourth year | 79 (26.6) | |
| Age group (years) | 18–21 | 96 (32.3) |
| 22–25 | 134 (45.1) | |
| 26–29 | 54 (18.2) | |
| ≥ 30 | 13 (4.4) |
No significant gender differences were observed in AI Readiness (t = 1.135, p = 0.258) or technology anxiety (t = − 0.752, p = 0.453). Age showed weak and non-significant correlations with both AI Readiness (r = − 0.092, p = 0.093) and technology anxiety (r = 0.052, p = 0.363). Although older students (above 30 years) demonstrated slightly lower AI Readiness scores in some cases, this relationship was not statistically significant. The mean technology anxiety score (after reversing negatively worded items) was 3.76 ± 0.54 on a 5-point scale, indicating a moderate to high level of anxiety. Specifically, 48.4% of students reported moderate anxiety and 36.5% reported high anxiety levels, while 15.1% exhibited low anxiety. Years of education showed a significant negative correlation with technology anxiety (r = − 0.121, p = 0.026), suggesting that students in higher academic years experienced lower levels of anxiety toward technology (Table 2).
Table 2.
Associations between demographic variables and main study variables (n = 297)
| Demographic variable | Category / Statistic | AI Readiness | Technology Anxiety |
|---|---|---|---|
| Gender | Male (n = 167) | 3.724 ± 0.60 | 3.725 ± 0.53 |
| Female (n = 130) | 3.637 ± 0.62 | 3.793 ± 0.55 | |
| p-value | 0.258 | 0.453 | |
| Educational level | Bachelor’s (n = 215) | 3.647 ± 0.62 | 3.793 ± 0.55 |
| Master’s (n = 82) | 3.799 ± 0.59 | 3.711 ± 0.52 | |
| p-value | 0.011 | 0.313 | |
| Age | r (p-value) | –0.092 (0.093) | 0.052 (0.363) |
| Academic year | r (p-value) | 0.104 (0.059) | –0.121 (0.026) |
| Marital status | p-value | 0.614 | 0.853 |
Values for gender and educational level are presented as mean ± SD; comparisons were made using independent samples t-test. Values for age and academic year are presented as Pearson correlation coefficient (r) with corresponding p-value in parentheses. Marital status was analyzed using independent t-test; only p-values are reported. Bold values indicate statistical significance (p < 0.05)
The mean AI Readiness score was 3.68 ± 0.61, reflecting an overall moderate to high level of readiness. Approximately 42.0% of students were classified as having high AI Readiness, 41.2% moderate readiness, and 16.8% low readiness. Senior nursing students tended to have higher AI Readiness scores (r = 0.104, p = 0.059), indicating a borderline association. Additionally, students enrolled in master’s degree demonstrated significantly higher AI Readiness scores compared to bachelor’s degree students (p = 0.011).
A strong and statistically significant inverse relationship was found between AI Readiness and technology anxiety (r = − 0.648, p < 0.0001). This correlation was significant at the 99.9% confidence level, indicating that higher levels of AI Readiness were associated with lower levels of technology anxiety among nursing students. These findings suggest that Enhancing AI knowledge appears to be associated with lower technology anxiety; however, causal conclusions cannot be drawn from this cross-sectional design (Table 3).
Table 3.
Descriptive statistics, level distribution, and Pearson correlation between technology anxiety and AI readiness (n = 297)
| Variable | Mean ± SD | Range | Low n (%) | Moderate n (%) | High n (%) | r (p-value) |
|---|---|---|---|---|---|---|
| Technology Anxiety | 3.76 ± 0.54 | 2.15–4.85 | 45 (15.1) | 144 (48.4) | 108 (36.5) | –0.648 (< 0.001) |
| AI Readiness | 3.68 ± 0.61 | 1.78–5.00 | 50 (16.8) | 122 (41.2) | 125 (42.0) | –0.648 (< 0.001) |
The Pearson correlation coefficient (r = − 0.648, p < 0.001) indicates a strong, negative, and statistically significant correlation between Technology Anxiety and AI Readiness
36.5% of students reported high Technology Anxiety, while 42.0% demonstrated high AI Readiness
The coefficient of determination (R² = 0.420) indicates that AI Readiness explains 42% of the variance in
AI Readiness was strongly correlated with all its subscales (r = 0.74–0.85) and negatively with Technology Anxiety (r = − 0.648). Table 4, AI Readiness was the only significant predictor of Technology Anxiety (β = − 0.648, p < 0.001), explaining 43% of the variance (R² = 0.430). Demographics were non-significant (Table 5).
Table 4.
Correlation matrix (Pearson’s r) – n = 297
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| 1. Tech Anxiety | 1 | ||||||
| 2. AI Readiness | − 0.648 | 1 | |||||
| 3. AI Cognition | − 0.61 | 0.85 | 1 | ||||
| 4. AI Ability | − 0.59 | 0.83 | 0.72 | 1 | |||
| 5. AI Vision | − 0.53 | 0.78 | 0.65 | 0.68 | 1 | ||
| 6. AI Ethics | − 0.49 | 0.74 | 0.58 | 0.61 | 0.59 | 1 | |
| 7. Academic Year | − 0.121 | 0.104 | 0.09 | 0.11 | 0.08 | 0.06 | 1 |
Note: Bold = p < 0.001; all correlations significant except Academic Year with subscales (p > 0.05)
Table 5.
Linear regression (predicting Tech Anxiety) – n = 297
| Predictor | B | SE | β | t | p |
|---|---|---|---|---|---|
| (Constant) | 4.87 | 0.12 | – | 40.58 | < 0.001 |
| AI Readiness | –0.32 | 0.03 | –0.648 | –14.21 | < 0.001 |
| Gender (Male) | 0.04 | 0.06 | 0.04 | 0.71 | 0.478 |
| Age | 0.01 | 0.01 | 0.03 | 0.52 | 0.603 |
| Academic Year | –0.03 | 0.02 | –0.08 | –1.38 | 0.169 |
| Marital (Single) | 0.02 | 0.07 | 0.02 | 0.31 | 0.759 |
| Education (Master’s) | –0.08 | 0.07 | –0.07 | –1.17 | 0.243 |
Model: R² = 0.430, Adj R² = 0.418, F (6,290) = 36.5, p < 0.001
Discussion
This study found a strong negative correlation between AI Readiness and technology anxiety among nursing students (r = − 0.648, p < 0.001), suggesting that students who perceive themselves as more prepared for AI tend to report lower anxiety toward technology. Although both variables were at moderate-to-high levels, more than half of the students had only moderate-to-low AI Readiness, indicating a need for educational interventions to strengthen AI-related competencies.
This finding is consistent with the results of several studies; for example, a study on nursing students in China showed that artificial intelligence readiness is significantly negatively correlated with artificial intelligence anxiety (r = − 0.64) [9].
This is in line with earlier systematic reviews, which have shown that anxiety can lower a person’s readiness for new technology or create a barrier to accepting it [20] This finding can be explained by Bandura’s theory of self-efficacy. According to this theory, when people have the right knowledge and skills, their confidence grows, which in turn reduces the fear that comes from not knowing or feeling uncertain about something new [21].
In the present study, both variables were reported at moderate to high levels, but more than half of the population had moderate to low readiness. These results are consistent with findings from studies in Jordan and Korea, in which nursing students showed moderate levels of readiness and anxiety [10, 22] Despite an overall positive attitude toward the potential of artificial intelligence, many students still feel uncertain when facing complex technical aspects [20].
Regarding demographic variables, the present study showed no differences based on gender, age, or marital status. This finding is consistent with the results of studies on Jordanian students [10] and Korean students [22], which reported no significant differences based on gender. However, inconsistent results have been observed in some other sources; for example, several studies have reported that male students consistently score higher in AI literacy and readiness than female students [23] Additionally, some research has shown that age has an inverse relationship with technology literacy [20] Although the correlation between academic year and AI Readiness was not statistically significant (r = 0.104, p = 0.059), a borderline association was observed. This suggests a possible trend that should be investigated further in future studies with larger samples. Studies have shown that third- and fourth-year students, have higher ethical awareness and readiness for AI, and lower levels of anxiety compared to first- and second-year students [22]. The reason for this may be that students in higher years have greater exposure to clinical training and technological tools in healthcare settings, which leads to increased digital literacy and reduced fear of technology [10]. Furthermore, having a graduate degree (e.g., master’s vs. bachelor’s) has been identified as a strong predictor of greater AI Readiness and a better understanding of its benefits in care management [24]. Overall, these results emphasize the importance of continuous and longitudinal integration of AI training into nursing curricula [20, 25] Strengthening technical skills and digital literacy not only increases students’ readiness for their professional future but also, by removing psychological barriers such as anxiety, paves the way for the effective acceptance and use of these technology [26]. Overall, both variables were reported at moderate to high levels; however, more than half of the study population demonstrated moderate to low levels of AI Readiness.
Limitations
The present study is limited in its ability to establish causal relationships between AI Readiness and technology anxiety due to a cross-sectional design; therefore, the findings reflect correlations rather than causation. Also, self-reported questionnaires could be affected by response bias, including socially desirable responses or participants’ subjective perceptions. Another limitation is that the study was conducted in a single university and focused on one academic field (Nursing), which limits the generalizability of the findings. Although standardized, validated questionnaires were used, the absence of measures of AI-related skills limits the comprehensive assessment of students’ AI Readiness.
Recommendations for future research
The present study suggests longitudinal studies to assess the AI Readiness changes and technology anxiety over time and in different educational grades. Also, interventional studies may lead to the improvement of AI-related knowledge and skills, which leads to a decrease in the level of technology anxiety. Future studies at several universities and in various fields of medical sciences are recommended to increase the generalizability. Moreover, a combination of qualitative and quantitative methods may provide an understanding of students’ attitudes, concerns, and experiences towards artificial intelligence and new technologies.
Conclusion
The results indicate that technology anxiety is significantly associated with nursing students’ perceived readiness to use AI. These findings suggest that reducing technology anxiety through targeted training and support may increase nursing students’ comfort and ability to work with AI in their future clinical roles. Addressing this anxiety during academic education could help prepare the next generation of nurses to adopt new technologies more confidently.
Acknowledgements
Not applicable.
Abbreviations
- AI
Artificial Intelligence
- ATAS
Abbreviated Technology Anxiety Scale
- MAIRS-MS
Medical Artificial Intelligence Readiness Scale for Medical Students
- SPSS
Statistical Package for Social Sciences
- VIF
Variance Inflation Factor
Author contributions
Conceptualization, S. HO, H.K. and M.A.; methodology, S. HO; formal analysis, S.HO.; investigation, H.K. and M.A; writing—original draft preparation, M.A. and H.K.; writing—review and editing, S.HO. and H.K; supervision, S.HO.
Funding
This research received no external funding.
Data availability
The dataset generated during this study is not publicly available to protect participant confidentiality but can be requested from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the Declaration of Helsinki (2024 revision). The protocol was reviewed and approved by the Ethics Committee of Qom University of Medical Sciences (Code: IR.MUQ.REC.1404.091). Written informed consent was obtained from all participants prior to data collection.
Consent for publication
Not applicable (no individual person’s data are presented).
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hajar Karimtabar and Mobin Alee contributed equally to this work.
References
- 1.Hussain S, Bhatti DE. Artificial intelligence and medical education. Annals King Edw Med Univ. 2022;28(1):3–6. [Google Scholar]
- 2.Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459–86. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 3.Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of artificial intelligence in nursing care: a systematic review. J Nurs Adm Manag. 2023;2023(1):3219127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Atalla ADG, El-Gawad Mousa MA, Hashish EAA, Elseesy NAM, Abd El kader Mohamed AI, Sobhi Mohamed SM. Embracing artificial intelligence in nursing: exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses. BMC Nurs. 2025;24(1):661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC Med Educ. 2021;21(1):112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Rony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: enhancing preparedness for the future. Nurs Open. 2024;11(1). [DOI] [PMC free article] [PubMed]
- 7.Hernández-Orallo J, Vold K, editors. AI extenders: the ethical and societal implications of humans cognitively extended by AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society; 2019.
- 8.Mohanasundari SK, Kalpana M, Madhusudhan U, Vasanthkumar K, Singh R, Vashishtha N, et al. Can artificial intelligence replace the unique nursing role? Cureus. 2023;15(12). [DOI] [PMC free article] [PubMed]
- 9.Fan J, Zhang L, Li N, Man S. How the perceived substitution of AI technology hinders nurses’ innovation behavior: the mediating role of AI anxiety and human-AI cooperation intention and the moderating role of organizational AI Readiness. BMC Nurs. 2025;24(1):832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Migdadi MK, Oweidat IA, Alosta MR, Al-Mugheed K, Saeed Alabdullah AA, Farghaly Abdelaliem SM. The association of artificial intelligence ethical awareness, attitudes, anxiety, and intention-to-use artificial intelligence technology among nursing students. Digit Health. 2024;10:20552076241301958. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Cugurullo F, Acheampong RA. Fear of AI: an inquiry into the adoption of autonomous cars in spite of fear, and a theoretical framework for the study of artificial intelligence technology acceptance. AI Soc. 2024;39(4):1569–84. [Google Scholar]
- 12.Almaiah MA, Alfaisal R, Salloum SA, Hajjej F, Thabit S, El-Qirem FA, et al. Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: Students’ perceptions at the university level. Electronics. 2022;11(22):3662. [Google Scholar]
- 13.Wang Y-M, Wei C-L, Lin H-H, Wang S-C, Wang Y-S. What drives students’ AI learning behavior: A perspective of AI anxiety. Interact Learn Environ. 2024;32(6):2584–600. [Google Scholar]
- 14.Meuter ML, Ostrom AL, Bitner MJ, Roundtree R. The influence of technology anxiety on consumer use and experiences with self-service technologies. J Bus Res. 2003;56(11):899–906. [Google Scholar]
- 15.Georgieva-Tsaneva G, Serbezova I, Beloeva S. Application of virtual reality, artificial intelligence, and other innovative technologies in healthcare education (nursing and midwifery specialties): Challenges and strategies. Educ Sci. 2024;15(1):11. [Google Scholar]
- 16.Božić V. Artifical Intelligence in nurse education. Eng Appl Artif Intell. 2024. pp. 143–72.
- 17.Wilson ML, Huggins-Manley AC, Ritzhaupt AD, Ruggles K. Development of the abbreviated technology anxiety scale (ATAS). Behav Res Methods. 2023;55(1):185–99. [DOI] [PubMed] [Google Scholar]
- 18.Razavi SM, Haji Alizadeh K, Amirfakhraei A. Psychometric properties of the Persian version of abbreviated technology anxiety scale: insights from university students in Iranian context. Interdisciplinary J Virtual Learn Med Sci. 2024;15(3):241–55. [Google Scholar]
- 19.Moodi Ghalibaf A, Moghadasin M, Emadzadeh A, Mastour H. Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). BMC Med Educ. 2023;23(1):577. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.El Arab RA, Alshakihs AH, Alabdulwahab SH, Almubarak YS, Alkhalifah SS, Abdrbo A, et al. Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Front Digit Health. 2025;7:1666005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bandura A. Self-efficacy: The exercise of control. New York: W.H. Freeman and Company; 1997.
- 22.Kwak Y, Ahn J-W, Seo YH. Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions. BMC Nurs. 2022;21(1):267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Sumengen AA, Subasi DO, Cakir GN. Nursing students’ attitudes and literacy toward artificial intelligence: a cross-sectional study. Teach Learn Nurs. 2025;20(1):e250–7. [Google Scholar]
- 24.Kotp MH, Ismail HA, Basyouny HAA, Aly MA, Hendy A, Nashwan AJ, et al. Empowering nurse leaders: readiness for AI integration and the perceived benefits of predictive analytics. BMC Nurs. 2025;24(1):56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hussein Mohamed S, Abed El-Rahman Mohamed M, Farouk Mahmoud S, HessienYousef Heggy E. The effect of educational program on nurses’ knowledge and attitude regarding artificial intelligence. Egypt J Health Care. 2023;14(2):1110–28. [Google Scholar]
- 26.Zhang J, Shi Y, Ma J, Luo C, Song M, Xu X, et al. The relationship between nursing undergraduates’ artificial intelligence readiness, learning motivation, and artificial intelligence anxiety: a mediation and network analysis study. 2025.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset generated during this study is not publicly available to protect participant confidentiality but can be requested from the corresponding author upon reasonable request.