Nursing students’ readiness for and acceptance of artificial intelligence technologies in clinical skills training: A cross-sectional study.

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Learn more: PMC Disclaimer | PMC Copyright Notice Int J Nurs Stud Adv . 2026 May 14;11:100567. doi: 10.1016/j.ijnsa.2026.100567 Nursing students’ readiness for and acceptance of artificial intelligence technologies in clinical skills training: A cross-sectional study Ali D Abousoliman a, ⁎ , Mohamed Gamal El-sehrawy a , Heba Gamal Elgamal b Author information Article notes Copyright and License information a College of Nursing, Prince Sattam bin Abdulaziz University, Alkharj city, Saudi Arabia b Lecturer of Nursing Administration, Faculty of Nursing, Kafrelsheikh University, Egypt ⁎ Corresponding author. Alidaif81@gmail.com Received 2026 Mar 12; Revised 2026 May 12; Accepted 2026 May 14; Collection date 2026 Dec. © 2026 The Author(s) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13234218  PMID: 42256512 Abstract Background: Artificial intelligence is increasingly transforming healthcare delivery and health professions education, particularly in clinical skills training and simulation-based learning environments. This transformation necessitates an evaluation of the readiness of future nurses. However, limited evidence exists regarding nursing students’ readiness and acceptance of artificial intelligence-based technologies in clinical skills training within Saudi universities. Aim The aim was to assess nursing students’ readiness and acceptance, and intention to use artificial intelligence-based healthcare technologies in clinical skills training. Design A cross-sectional descriptive correlational design was used. Methods A self-administered online questionnaire was distributed to a convenience sample of 747 undergraduate nursing students across 10 universities in Saudi Arabia. The survey measured artificial intelligence readiness (cognition, technical ability, vision, and ethics) and artificial intelligence acceptance (perceived usefulness, perceived ease of use, attitude, behavioral intention), along with demographic and educational data. Data were analyzed using descriptive statistics, independent t -tests, one-way analysis of variance, Pearson correlation, and multiple linear regression analysis. Results Participants demonstrated a moderate to high level of overall readiness and acceptance for artificial intelligence in clinical training. The highest readiness scores were observed in the vision and ethics domains, whereas technical ability was the lowest. For acceptance, attitude and behavioral intention were the highest-rated subdomains. Academic year was positively associated with both readiness and acceptance, with more advanced students demonstrating higher levels. Participants with prior exposure to artificial intelligence demonstrated significantly higher readiness and acceptance scores than those without prior exposure, although the magnitude of the association was small. A moderate positive relationship was also observed between readiness and acceptance. Conclusions and implications Participating nursing students demonstrated conceptual and ethical preparedness for artificial intelligence integration but reported gaps in technical competence. Academic progression was associated with higher readiness and acceptance, while prior exposure was also associated with more favorable readiness and acceptance outcomes. We suggest that Saudi nursing students may have lower technical skills and practical competence compared with their conceptual, ethical, and attitudinal readiness for artificial intelligence, highlighting the need for further attention to technical training within artificial intelligence education. Keywords: Artificial intelligence, Nursing students, Technology acceptance, Readiness, Nursing education, Curriculum development What is already known: • Artificial intelligence is increasingly being integrated into healthcare systems and health profession education. • Previous researchers have indicated that healthcare students generally hold positive attitudes toward artificial intelligence and recognize its potential to improve clinical decision-making and patient care. • However, students’ technical competence and practical readiness to use artificial intelligence technologies may not fully match their positive perceptions. Alt-text: Unlabelled box dummy alt text What this paper adds. • Empirical evidence on nursing students’ readiness and acceptance of artificial intelligence-based healthcare technologies in clinical skills training in Saudi Arabia. • While participants demonstrated moderate to high acceptance and positive perceptions of artificial intelligence, their technical readiness was relatively lower than their conceptual, ethical, and attitudinal readiness within the same sample. • The findings highlight a positive association between academic progression and artificial intelligence readiness and acceptance, suggesting that exposure to clinical education may influence students’ perceptions of emerging healthcare technologies. Alt-text: Unlabelled box dummy alt text 1. Introduction The incorporation of artificial intelligence in the healthcare system is transforming the way patients are cared for, offering improved diagnostic capabilities, customized treatments, and optimized healthcare efficiency ( Almalki et al., 2025 ; Ibrahim, 2025 ). However, the successful implementation of these technologies depends not only on their technical capabilities but also on the preparedness of healthcare professionals, particularly future practitioners such as nursing students, to effectively adopt and utilize them ( Al-Olaimat et al., 2025 ). Artificial intelligence refers to computational systems capable of performing tasks that typically require human intelligence ( Lee, 2015 ). Global development of artificial intelligence has been driven by countries with substantial research and investment capacity ( Christie et al., 2021 ). These nations dominate in foundational research, massive private investment, and the development of large-scale language models and commercial applications ( Stanford Institute for Human-Centered, 2023 ). In contrast, the Kingdom of Saudi Arabia has prioritized adoption and implementation of artificial intelligence across strategic sectors. This national strategy is a central pillar of its Saudi Vision 2030 framework, with the government actively fostering a robust ecosystem through national initiatives like the Saudi data and artificial intelligence authority ( Saudi Data and Artificial Intelligence Authority, 2025 ). Recent researchers have emphasized the importance of artificial intelligence readiness, literacy, and awareness among students, highlighting their role in shaping attitudes toward artificial intelligence technologies ( Boztepe et al., 2025 ; Semerci Şahin et al., 2025 ). In the Kingdom of Saudi Arabia, the Ministry of Education has rolled out an artificial intelligence curriculum aimed at over six million learners, reflecting a national emphasis on technological innovation, including the future of the healthcare system ( Ministry of Education, 2025 ). Nursing schools play an important role in preparing future healthcare providers to work effectively in an environment characterized by increasingly complex healthcare technologies ( Alenazi and Alhalal, 2025 ). Understanding nursing students' readiness to adopt artificial intelligence-based healthcare technologies may help inform effective integration of these tools into clinical skills training ( Al Shahrani, 2024 ; Oweidat, 2025 ). In the present study, readiness was conceptualized as a multidimensional construct encompassing cognition, technical ability, vision, and ethics. However, readiness alone may not be sufficient; students’ acceptance of artificial intelligence technologies is also important in determining their willingness to adopt these tools into clinical practice. This perspective is supported by the Technology Acceptance Model, which emphasizes the role of perceived usefulness and ease of use in shaping technology adoption ( Saflor, 2025 ). Together, these constructs provide a framework for understanding both preparedness for and receptiveness to artificial intelligence technologies. Despite the increasing integration of artificial intelligence within healthcare systems and educational initiatives in Saudi Arabia, evidence regarding nursing students’ preparedness and acceptance of artificial intelligence-based healthcare technologies in clinical skills training remains limited. Existing Saudi evidence is also limited by smaller multi-site samples and differing readiness frameworks. Moreover, researchers have often examined these constructs separately or in different populations, limiting a comprehensive understanding of their interaction within nursing education contexts. In the present study, we used an independent dataset from a larger multi-university sample and extended previous work by simultaneously examining readiness, acceptance, and intention to use while applying an artificial intelligence-specific readiness framework. Assessing students’ preparedness and receptiveness was, therefore, important for educational planning. To our knowledge, no previous Saudi study has examined these constructs simultaneously across a 10-university nursing student sample using an artificial intelligence-specific readiness framework. Therefore, the aim of this study was to assess nursing students’ readiness, acceptance, and intention to use artificial intelligence-based healthcare technologies in clinical skills training in Saudi Arabia. We hypothesized that higher academic year and prior exposure would be associated with greater readiness and acceptance. We sought to achieve the following objectives: • Assess the level of nursing students’ readiness, acceptance, and intention to use artificial intelligence-based healthcare technologies in clinical skills training in Saudi Arabia. • Identify demographic and educational factors influencing nursing students’ readiness dimensions, acceptance outcomes and intention to use artificial intelligence technologies. • Examine the associations between demographic and educational factors and students’ readiness, acceptance, and intention to use artificial intelligence-based healthcare technologies. 2. Review of literature The integration of artificial intelligence technology in healthcare education has received increasing attention because of its potential to enhance teaching and learning processes ( El-Ashry et al., 2025 ; El Arab, 2025 ). Artificial intelligence applications, such as virtual simulation and intelligent tutoring systems, may provide nursing students with opportunities for complex simulation-based experiential learning and decision-making practice ( Almalki et al., 2025 ; Rony et al., 2025 ). These technologies contribute to the development of higher-order cognitive competencies essential for clinical reasoning. Researchers have also pointed out the potential capabilities of artificial intelligence in improving the process of customized or personalized learning, adapted to students' pace, and needs ( Kumar et al., 2025 ; Badawy et al., 2025 ). This suggests that artificial intelligence may help connect theoretical knowledge with clinical practice. Researchers have suggested that artificial intelligence may improve efficiency, decision-making support, and aspects of quality care when appropriately integrated into education and practice ( Khosravi et al., 2024 ). Simulation-based artificial intelligence learning has also been associated with greater confidence and preparedness in clinical scenarios ( Badawy et al., 2025 & Alsaedi et al., 2024 ). However, the extent to which these outcomes depend on students’ readiness and acceptance of artificial intelligence remains insufficiently explored. In Saudi Arabia, the adoption of artificial intelligence within nursing education is gradually increasing. According to Al-Olaimat et al. (2025) , most nursing students reported generally-positive attitudes toward artificial intelligence technology, with 99.4% accepting the application of artificial intelligence technology-based platforms in their educational process, but discomfort, negative attitudes, and other concerns also emerged, requiring appropriate interventions. This indicates that students’ perceptions of artificial intelligence were not uniformly positive but rather complex and multidimensional. Faculty readiness may support effective teaching environments and facilitate students’ preparation for technology-enabled healthcare settings ( Al Shahrani, 2024 ; Al-Olaimat et al., 2025 ). In this context, educators play a central role in facilitating effective technology integration by shaping students’ competencies, attitudes, and readiness for digital healthcare environments ( Khan Rony et al., 2025 ). This trend also aligns with national digital transformation priorities in Saudi Arabia ( Ministry of Education, 2025 ; Alsaedi et al., 2024 ). Evaluating readiness and acceptance levels among nursing students remains important for understanding potential adoption of artificial intelligence technologies. Almalki et al. (2025) reported moderate readiness among the students in terms of cognition, technical ability, vision, and ethics, with many students reporting that artificial intelligence-related courses should be included in nursing education. Other researchers have reported associations between readiness and demographic variables such as age and sex, with older and female nursing students registering greater readiness ( Alenazi and Alhalal, 2025 ). Prior technology experience has also been associated with greater confidence in artificial intelligence-supported learning contexts ( Al Shahrani, 2024 ; El Arab et al., 2025 ). Nevertheless, the strength and consistency of these relationships remain variable across studies, indicating the need for further investigation. Despite potential benefits, several barriers may limit artificial intelligence in nursing education ( El-Ashry et al., 2025 ). Technical challenges, concerns regarding confidentiality, or fears of professional displacement have been reported ( El-Ashry et al., 2025 ). However, the resistance to change among faculty members and lack of exposure to the resources available in the field of artificial intelligence are other challenges that are hampering the integration of artificial intelligence in nursing education ( Ramadan et al., 2024 ). Importantly, previous researchers have tended to examine readiness dimensions and acceptance outcomes independently, with limited attention to their combined influence within nursing education contexts, highlighting an evidence gap that the present study sought to examine. Few Saudi studies have examined readiness, acceptance, and intention to use artificial intelligence simultaneously across large multi-university nursing student samples. Collectively, prior researchers have highlighted a growing interest in artificial intelligence among nursing students but have found mixed results regarding readiness dimensions and associated predictors ( Alenazi and Alhalal, 2025 ; Al Shahrani, 2024 ). These uncertainties support the need for further multi-institutional research in Saudi Arabia. 3. Methods 3.1. Research design A cross-sectional descriptive correlational design was employed. 3.2. Study setting Across the Kingdom of Saudi Arabia, approximately 17,085 students are currently enrolled in bachelor of science in nursing programs within higher education institutions, with female students comprising most enrollments. Approximately 14 public universities provide bachelor of nursing degrees, with >39 nursing programs, across both public and private institutions, distributed throughout the Kingdom. We conducted this study at 10 nursing colleges within 10 public universities in different regions of Saudi Arabia. The institutions were purposively selected to represent diverse educational settings and geographical regions, thereby enhancing the contextual relevance of the findings, while improving variability in institutional contexts; however, full national generalization cannot be assumed. 3.3. Study population The target population included undergraduate nursing students enrolled in clinical skills training courses at the time of recruitment. A non-probability convenience sampling method was employed, inviting students who were readily accessible and willing to participate. This approach was adopted due to practical and logistical considerations, particularly the wide geographical distribution of participating universities and the use of an online survey platform for data collection. Convenience sampling facilitated timely data collection and enabled access to a large and diverse group of nursing students while minimizing resource constraints, although this approach may introduce selection bias and limit the generalizability of the findings. 3.4. Inclusion and exclusion criteria The inclusion criteria comprised undergraduate nursing students enrolled in accredited nursing programs at the participating universities. Eligible participants were those who were actively involved in clinical skills training during the data collection period and who agreed to participate by completing the online survey. Students who had already completed their nursing program or who were not engaged in clinical training during the study period were excluded. 3.5. Sample size Sample size adequacy was evaluated in relation to the planned multiple linear regression analyses. Following commonly used recommendations for regression analysis, the minimum sample size for testing the overall model can be estimated as N ≥ 50 + 8 m, where m represents the number of predictors, and the minimum sample size for testing individual predictors can be estimated as N ≥ 104 + m. In the present study, four predictors were entered into the regression models: sex, academic year, prior exposure to artificial intelligence, and frequency of technology use. Accordingly, the minimum required sample size was 82 participants for the overall regression model and 108 participants for testing individual predictors. The achieved sample size of 747 participants substantially exceeded these requirements and was, therefore, considered adequate for the planned regression analyses. The sample size also provided sufficient precision for descriptive estimates and correlation analyses. 3.6. Data collection tool A structured, self-administered questionnaire in English was used; the questionnaire was administered in English because it is the official language of instruction in nursing programs in Saudi universities. Nursing students are routinely taught and assessed using English-language course materials, clinical terminology, and examinations; therefore, English was considered appropriate for the target population. These questionnaires consisted of three main sections: 3.6.1. Section I: demographic and educational characteristics This section collected demographic and educational background information, including sex, academic year, prior experience with digital technologies, and previous exposure to artificial intelligence-related courses. 3.6.2. Section II: artificial intelligence readiness Artificial intelligence readiness was assessed using the Medical Artificial Intelligence Readiness Scale for medical students developed by Karaca et al. (2021) . This questionnaire included four subdomains: cognition (8 items), technical ability (8 items), vision (3 items), and ethics (3 items). Responses were measured using a 5-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). No predefined cut-off scores were applied; instead, interpretation was based on descriptive statistics and mean score distributions. The original study reported good internal consistency (Cronbach’s α = 0.89) ( Karaca et al., 2021 ). In the present study, internal consistency was also assessed and found to be acceptable (α = 0.87) Although originally developed for medical students, the instrument was considered appropriate for nursing students due to the overlap in clinical training contexts and competencies. Minor wording adaptations were made to ensure contextual relevance, and content clarity was confirmed during the pilot phase. 3.6.3. Section III: artificial intelligence acceptance Artificial intelligence acceptance and attitudes were assessed using constructs derived from the Technology Acceptance Model developed by Davis et al. (1989) . This section measured perceived usefulness (six items), perceived ease of use (six items), attitude toward artificial intelligence use (four items), and behavioral intention to use artificial intelligence (three items). Responses were recorded using 5-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Internal consistency reliability for the subscales was high, with Cronbach’s α values ≥ 0.86 in the current study. The selection of these constructs was conceptually informed by the Technology Acceptance Model, which posits that users’ acceptance of new technologies is influenced by perceived usefulness, perceived ease of use, and behavioral intention. However, no standardized scoring cut-off values exist within Technology Acceptance Model; therefore, data were analyzed using descriptive and inferential statistical methods. 3.7. Pilot study A pilot study was conducted at the first author’s university with 30 undergraduate nursing students to assess the clarity, feasibility, and reliability of the questionnaire. Data obtained from the pilot study were excluded from the main analysis to avoid potential data contamination. Participants completed the instrument measuring the various constructs, and minor wording adjustments were made based on their feedback. The questionnaire demonstrated excellent internal consistency, confirming its clarity, reliability, and suitability for the main study. 3.8. Data collection procedure Approval to conduct the study was obtained from the administrations of all participating colleges before initiating data collection. Participation in the study was voluntary. Data were collected using an online survey administered through Google Forms. The survey link was distributed within each participating university through official student communication channels, including academic coordinators and student WhatsApp groups. Before accessing the questionnaire, participants were provided with an information sheet explaining the purpose and nature of the study. Informed consent was obtained electronically from all participants prior to completing the questionnaire. The survey remained open for 5 weeks, from September 7, 2025, to October 16, 2025. Weekly reminders were sent via WhatsApp through university communication channels to encourage participation. Because the survey link was disseminated through institutional and student communication channels rather than a closed individual invitation list, an exact response rate could not be calculated. To reduce duplicate responses, participants were instructed to complete the questionnaire only once, and repeated submissions were restricted where feasible through Google Forms settings. All responses were self-reported. Participants were informed that they had the right to withdraw from the study at any time without any obligation or consequence. 3.9. Data analysis Data were analyzed using IBM SPSS Statistics, version 27. Descriptive statistics, including frequencies, percentages, means, and standard deviations, were used to describe demographic characteristics as well as levels of artificial intelligence’ readiness and acceptance. Inferential statistical tests, including independent t -tests, one-way analysis of variance (ANOVA), and Pearson correlation analysis, were conducted to examine relationships between demographic variables and artificial intelligence readiness and acceptance. Pearson correlation analysis was applied only to continuous variables, while group differences across categorical variables were examined using t -tests and ANOVA, as appropriate. In addition, multiple linear regression analyses were conducted to identify independent predictors of artificial intelligence readiness and acceptance while controlling for potential confounding variables. The analyses were conducted at the individual student level, and institutional clustering was not modeled using multilevel or cluster-adjusted methods. Statistical significance was set at p ≤ 0.05. 3.10. Ethical considerations Ethical approval was obtained from the Standing Committee of Bioethical Research at Prince Sattam bin Abdulaziz University (Approval No SCBR-601/2025). Confidentiality and anonymity were strictly maintained throughout the study. The survey was conducted anonymously, and no personal identifiable information was collected from participants. All collected data were accessible only to the research team and were used solely for research purposes. Participants were informed about the aim of the study and their voluntary nature of participation. All responses were treated confidentially and were used only for research purposes. 4. Results The demographic and educational characteristics of the participants are summarized in Table 1 . A total of 747 undergraduate nursing students participated in the study. The sample was predominantly female, whereas males represented approximately one-quarter. Participants were drawn from several universities across Saudi Arabia. The largest proportion of students were from Prince Sattam bin Abdulaziz University, followed by Imam Abdulrahman bin Faisal University, Umm Al-Qura University, and King Saud University, while other universities contributed smaller proportions ranging from 6% to 10%. Regarding academic level, the distribution of students was relatively balanced across academic years. Second-year students represented the largest proportion, followed by first-year students, third-year students, and fourth-year students. Most participants reported prior exposure to artificial intelligence technologies. In terms of technology use frequency, approximately one-third-of participants reported frequent use, whereas nearly one-quarter indicated that they used technology either sometimes or always. Table 1. Descriptive statistics of participants’ personal characteristics. Items Frequency Percent Cumulative Percent University Tabuk University 48 6.4 6.4 Prince Sattam bin Abdulaziz University 193 25.8 32.3 Princess Nourah University 57 7.6 39.9 King Saud University 65 8.7 48.6 Umm Al-Qura University 70 9.4 58.0 Jizan University 58 7.8 65.7 King Khaled University 59 7.9 73.6 Imam Abdulrahman Bin Faisal University 76 10.2 83.8 Taif University 63 8.4 92.2 King Abdulaziz University 58 7.8 100.0 Sex Male 212 28.4 28.4 Female 535 71.6 100.0 Academic year First 195 26.1 26.1 Second 198 26.5 52.6 Third 184 24.6 77.2 Fourth 170 22.8 100.0 Prior exposure to artificial intelligence Yes 622 83.3 83.3 No 125 16.7 100.0 Frequency of technology use Rarely 98 13.1 13.1 Sometimes 210 28.1 41.2 Often 265 35.5 76.7 Always 174 23.3 100.0 Open in a new tab Table 2 presents the descriptive statistics for the main study variables, including artificial intelligence readiness and acceptance. Overall, participants demonstrated moderate levels of artificial intelligence readiness and acceptance of artificial intelligence-based healthcare technologies. Among the readiness subdomains, vision recorded the highest mean score, followed by ethics, cognition, and technical ability. For the acceptance subdomains, Attitude toward artificial intelligence use showed the highest mean score, followed by Intention to use artificial intelligence, Perceived usefulness, and Perceived ease of use. Table 2. Descriptive statistics for the overall mean of the study variable and its subdomains. Minimum Maximum Mean Std. Deviation Cognition Domain 2.13 4.00 3.0984 .32942 Technical ability Domain 2.13 3.75 2.9866 .30876 Ethical Domain 1.67 4.67 3.1749 .50146 Vision Domain 1.67 4.67 3.2066 .51258 Readiness means 2.41 3.77 3.0829 .23803 Perceived usefulness 2.00 4.17 3.1107 .35980 Perceived ease of use 2.00 4.00 2.9320 .36889 Attitude toward artificial intelligence use 2.00 4.25 3.1988 .40561 Intention to use 1.67 4.67 3.1937 .49210 Acceptance means 2.42 3.74 3.0859 .21752 Open in a new tab As shown in Table 3 , Group differences in artificial intelligence readiness and acceptance were examined using appropriate inferential tests. No statistically significant differences were observed between male and female participants, indicating comparable perceptions across sex groups. Table 3. Differences in artificial intelligence readiness and acceptance according to participants’ demographic and educational characteristics. Variable Category Readiness (Mean ± SD) p -value Acceptance (Mean ± SD) p -value Sex Male 3.07 ± 0.23 0.724 3.08 ± 0.21 0.798 Female 3.08 ± 0.23 3.08 ± 0.21 Academic year First 2.94 ± 0.22 <0.001 2.94 ± 0.20 <0.001 Second 3.05 ± 0.21 3.06 ± 0.17 Third 3.13 ± 0.22 3.14 ± 0.20 Fourth 3.22 ± 0.19 3.21 ± 0.19 Prior exposure to artificial intelligence Yes 3.09 ± 0.23 0.011 3.09 ± 0.21 0.014 No 3.03 ± 0.22 3.04 ± 0.20 Frequency of technology use Rarely 3.05 ± 0.23 0.417 3.08 ± 0.21 0.786 Sometimes 3.10 ± 0.23 3.08 ± 0.22 Often 3.07 ± 0.25 3.09 ± 0.21 Always 3.07 ± 0.20 3.07 ± 0.22 Open in a new tab Independent samples t -tests were used to examine differences in binary variables, including sex and prior exposure. One-way analysis of variance (ANOVA) was applied to assess differences across multi-category variables, such as academic year and frequency of technology use. Statistical significance was set at p ≤ 0.05. SD (standard deviation) represents the variability of scores around the mean. In contrast, statistically-significant differences were identified across academic years for both readiness and acceptance. A clear increasing trend was observed, with participants at more advanced academic levels demonstrating higher levels compared with those in earlier years. Post hoc comparisons confirmed that these differences were consistent across multiple academic levels. Tukey Honestly Significant Difference Post Hoc Comparisons were conducted to identify pairwise differences across academic years. For both artificial intelligence readiness and acceptance, all pairwise comparisons between academic years were statistically significant ( p ≤ 0.004). Scores increased progressively across academic years, with fourth-year participants reporting the highest readiness and acceptance scores and first-year participants reporting the lowest. Participants with prior exposure to artificial intelligence reported significantly higher readiness and acceptance compared with those without such exposure. However, the magnitude of these differences was small, suggesting limited practical significance. No statistically significant differences were found across levels of technology use for either readiness or acceptance, indicating that general technology use frequency was not associated with these constructs. As shown in Table 4 , Using correlation analysis, we found a statistically-significant positive relationship between overall artificial intelligence readiness and acceptance, indicating that higher readiness was associated with greater acceptance of artificial intelligence technologies. Table 4. Correlations between artificial intelligence readiness and acceptance and their subdomains. Variables Cognition Ability Vision Ethics Total Readiness mean Perceived usefulness Perceived ease of use Attitude toward use Intention to use Readiness Ability Pearson Correlation 0.177 ⁎⁎ 1 Sig. (2-tailed) 0.000 Vision Pearson Correlation 0.678 ⁎⁎ 0.094 * 1 Sig. (2-tailed) 0.000 0.010 Ethics Pearson Correlation 0.067 0.049 0.071 1 Sig. (2-tailed) 0.065 0.177 0.053 Total Readiness mean Pearson Correlation 0.805 ⁎⁎ 0.603 ⁎⁎ 0.700 ⁎⁎ 0.365 ⁎⁎ 1 Sig. (2-tailed) 0.000 0.000 0.000 0.000 Acceptance Perceived usefulness Pearson Correlation 0.404 ⁎⁎ 0.115 ⁎⁎ 0.127 ⁎⁎ 0.114 ⁎⁎ 0.328 ⁎⁎ 1 Sig. (2-tailed) 0.000 0.002 0.000 0.002 0.000 Perceived ease of use Pearson Correlation 0.132 ⁎⁎ 0.138 ⁎⁎ 0.068 0.101 ⁎⁎ 0.180 ⁎⁎ 0.066 1 Sig. (2-tailed) 0.000 0.000 0.064 0.006 0.000 0.071 Attitude toward use Pearson Correlation 0.139 ⁎⁎ 0.097 ⁎⁎ 0.129 ⁎⁎ 0.036 0.164 ⁎⁎ 0.087 * 0.142 ⁎⁎ 1 Sig. (2-tailed) 0.000 0.008 0.000 0.320 0.000 0.017 0.000 Intention to use Pearson Correlation 0.057 0.037 0.041 0.068 0.078 * 0.047 0.026 −0.004 1 Sig. (2-tailed) 0.117 0.309 0.266 0.062 0.033 0.203 0.486 0.914 Total Acceptance mean Pearson Correlation 0.357 ⁎⁎ 0.185 ⁎⁎ 0.168 ⁎⁎ 0.153 ⁎⁎ 0.360 ⁎⁎ 0.609 ⁎⁎ 0.635 ⁎⁎ 0.513 ⁎⁎ 0.394 ⁎⁎ Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Open in a new tab ⁎ Correlation is significant at the 0.05 level (2-tailed). ⁎⁎ Correlation is significant at the 0.01 level (2-tailed). Sig. represents the p -value used to determine statistical significance. At the subdomain level, the pattern of associations varied. Within readiness dimensions, stronger relationships were observed among cognition, vision, and total readiness, whereas ethics and some technical ability associations were weaker or non-significant. Across readiness and acceptance dimensions, some components, particularly those related to perceived usefulness, showed stronger associations, whereas relationships involving behavioral intention were generally weaker, and associations involving perceived ease of use were mixed across subdomains. Overall, from these findings, we suggest that the relationship between readiness and acceptance is multidimensional and varies across specific cognitive and attitudinal components, rather than being uniformly distributed across all subdomains. Table 5 shows Multiple linear regression analysis was conducted to identify predictors of artificial intelligence readiness. The overall model was statistically significant, F (4742) = 44.669, p < 0.001, explaining 19.4% of the variance in readiness, as indicated by the coefficient of determination (R² = 0.194; adjusted R² = 0.190). Table 5. Multiple linear regression analysis of factors associated with artificial intelligence’s readiness and acceptance. Variable Readiness B SE 95% CI β p -value Acceptance B SE 95% CI β p-value Sex 0.008 0.017 −0.026 to 0.042 0.015 0.658 −0.004 0.016 −0.035 to 0.027 −0.008 0.808 Academic Year 0.093 0.007 0.079 to 0.106 0.430 <0.001 0.088 0.006 0.075 to 0.101 0.448 <0.001 Prior exposure to artificial intelligence −0.056 0.021 −0.097 to −0.014 −0.087 0.008 −0.049 0.019 −0.087 to −0.012 −0.085 0.010 Frequency of technology use 0.002 0.008 −0.014 to 0.018 0.007 0.821 −0.002 0.007 −0.017 to 0.012 −0.010 0.765 Open in a new tab Readiness model: multiple correlation coefficient (R) = 0.441, coefficient of determination (R²) = 0.194, Adjusted R² = 0.190, F(4742) = 44.669, p < 0.001. Acceptance model: R = 0.457, R² = 0.209, Adjusted R² = 0.205, F (4742) = 48.960, p < 0.001. Note: B = unstandardized regression coefficient; β = standardized regression coefficient; CI = confidence interval. Sex was coded as 1 = male and 2 = female. Academic year was coded in ascending order from 1 = first year to 4 = fourth year. Prior exposure to artificial intelligence was coded as 1 = yes and 2 = no. Frequency of technology use was coded in ascending order from lower to higher frequency. Academic year emerged as a strong positive predictor, with higher academic levels associated with increased readiness. Prior exposure to artificial intelligence was also identified as a statistically-significant predictor; because prior exposure was coded as 1 = yes and 2 = no, this finding indicates that participants with prior exposure had higher readiness scores than those without prior exposure. In contrast, sex and frequency of technology use were not significant predictors. A second regression model was conducted to examine predictors of artificial intelligence acceptance. This model was also statistically significant, F (4742) = 48.960, p < 0.001, explaining 20.9% of the variance in acceptance (R² = 0.209; adjusted R² = 0.205). Academic year remained a strong positive predictor of acceptance. Prior exposure to artificial intelligence was also statistically significant, indicating that participants with prior exposure demonstrated higher acceptance scores than those without. Sex and frequency of technology use were not significantly associated with acceptance. 5. Discussion We have revealed a nuanced pattern of readiness and acceptance among Saudi nursing students regarding artificial intelligence in clinical training. This pattern reflected a partial alignment between participants’ perceptions of artificial intelligence and their actual preparedness to apply it in clinical contexts. Overall, there were moderate to high levels of both readiness and acceptance. However, a closer examination of the readiness subdomains revealed a notable variation between participants’ conceptual perceptions of artificial intelligence and their perceived technical competence. Specifically, the domains of vision and ethics demonstrated relatively higher scores compared with cognition and technical ability. This discrepancy suggests that students may conceptually endorse artificial intelligence while lacking sufficient practical competence to engage with it effectively. This pattern aligns with emerging regional evidence. Researchers conducting studies among Saudi healthcare students have reported similar findings, indicating that, although students recognize the potential value of artificial intelligence in healthcare, their perceived technical competence and conceptual understanding remain less developed than their positive attitudes and general acceptance of artificial intelligence ( Almalki et al., 2025 ; Amiri et al., 2024 ). These findings are consistent with recent evidence demonstrating that artificial intelligence readiness is closely associated with students’ literacy and attitudinal orientations toward artificial intelligence technologies ( Boztepe et al., 2025 ). We interpret these findings as suggesting that students may hold favorable perceptions regarding the future role of artificial intelligence in healthcare, while simultaneously feeling insufficiently prepared to apply these technologies in clinical practice. Similar observations have also been reported internationally, where enthusiasm for emerging technologies often exceeds the current level of educational preparation within healthcare curricula ( Booth et al., 2021 ). Within the Saudi context, this pattern may reflect the broader national emphasis on technological transformation under Saudi Vision 2030. National initiatives have increased awareness of artificial intelligence and its potential role in transforming healthcare systems. Consequently, students may reflect growing awareness of the future role of artificial intelligence in healthcare. However, the comparatively low scores in technical ability and cognition may indicate that the integration of artificial intelligence-related competencies within nursing curricula is still developing. Researchers have shown that targeted educational interventions, including structured artificial intelligence-related courses, can significantly enhance nursing students’ cognitive understanding and technical competence related to artificial intelligence applications ( Aldhobaib, 2025 ). Regarding artificial intelligence acceptance, the participants demonstrated moderate levels of acceptance, with relatively higher scores for perceived usefulness and behavioral intention to use artificial intelligence. These findings are consistent with the core assumptions of the Technology Acceptance Model, which proposes that perceived usefulness plays a central role in shaping individuals’ attitudes and intentions toward adopting new technologies ( Davis, 1989 ). In addition, recent scale development studies have highlighted the importance of artificial intelligence awareness as a distinct construct influencing students’ engagement with emerging technologies, further supporting the multidimensional nature of artificial intelligence readiness and acceptance ( Semerci Şahin et al., 2025 ). This alignment supports the conceptual relevance of the Technology Acceptance Model in explaining participants’ acceptance patterns in the present study. Similar findings have been reported in previous studies, indicating that healthcare students generally perceive artificial intelligence technologies as beneficial tools for improving clinical decision-making and enhancing patient safety ( Alsulami et al., 2024 ). Interestingly, ethical concerns did not appear to substantially limit artificial intelligence acceptance. This finding differs from some international reviews that have identified ethical concerns, including issues related to data privacy and algorithmic transparency, as potential barriers to artificial intelligence adoption in healthcare ( Cucci et al., 2025 ). However, these interpretations require further empirical investigation and were not directly measured in the present study. Therefore, such explanations should be interpreted cautiously. We demonstrated a statistically-significant positive correlation between academic year and both readiness and acceptance. This suggests that students in more advanced academic levels may develop greater familiarity with emerging technologies through progressive exposure to clinical education and digital tools. Similar patterns have been reported in previous studies, indicating that increased academic progression and exposure to clinical environments may contribute to more favorable perceptions toward healthcare technologies ( Al-Olaimat et al., 2025 ; Al Omari et al., 2024 ). This interpretation is further reinforced by the regression analysis, in which academic year emerged as the strongest independent predictor of both readiness and acceptance, highlighting the cumulative effect of educational progression. Prior exposure to artificial intelligence was significantly associated with higher readiness and acceptance among nursing participants. Although the magnitude of these associations was small in the regression models, we suggest that previous familiarity with artificial intelligence technologies may contribute to greater confidence, perceived competence, and openness toward their educational use. Similar patterns have been reported in studies examining students’ experiences with emerging digital technologies, where prior experience was linked to more favorable perceptions and greater willingness to engage with new tools ( Yeung et al., 2025 ). This association was further supported by the regression analysis, in which prior exposure remained a statistically-significant predictor after adjustment for other variables. However, the modest effect size suggests that exposure alone may be insufficient without meaningful or structured educational engagement. Variability in the quality, depth, and relevance of prior exposure may help explain why the observed relationship, although significant, was relatively weak. In addition, some statistically-significant coefficients were small in magnitude, which may partly reflect the relatively large sample size of the present study. Furthermore, the positive correlation observed between overall readiness and acceptance is consistent with theoretical frameworks, such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, that suggest that individuals’ familiarity and competence with technology are closely associated with their intention to adopt and use it ( Venkatesh et al., 2003 ). Perceived usefulness and perceived ease of use also showed strong positive associations with overall acceptance, consistent with established technology acceptance frameworks. This pattern may indicate the potential value of strengthening both conceptual understanding and practical competence when preparing nursing students for the integration of artificial intelligence technologies into healthcare practice. Previous researchers have emphasized that foundational knowledge of artificial intelligence concepts is essential for enabling healthcare students to recognize its potential applications in clinical settings ( Vaidya et al., 2023 ; Booth et al., 2021 ). Another notable finding was the weak relationship between certain acceptance constructs, particularly between behavioral intention to use artificial intelligence and perceived usefulness. Although the correlation was positive, it was not statistically significant. This may suggest that perceiving artificial intelligence as useful does not necessarily translate into stronger behavioral intention within this sample. Additional factors not measured in the present study may influence intention to use. Researchers have indicated that additional factors, such as confidence in using technology, perceived competence, and opportunities for practical training, may influence the extent to which positive attitudes translate into actual behavioral intentions ( Gao et al., 2022 ; Neff et al., 2022 ; O'Connor et al., 2023 ). Finally, these findings may represent an important educational consideration within the broader context of ongoing healthcare transformation in Saudi Arabia. National initiatives aimed at integrating advanced digital technologies into healthcare services may increase awareness and interest in artificial intelligence applications among healthcare students. However, the practical integration of artificial intelligence technologies within clinical education and training environments may still be evolving ( Alkhamis et al., 2023 ). Therefore, the observed gap between conceptual awareness and practical competence may represent an important consideration for nursing education. Accordingly, these findings may indicate the potential value of more structured, competency-oriented educational approaches. Strengthening the integration of artificial intelligence-related training within nursing education programs may, therefore, support students’ preparedness for future technology-enabled clinical practice. Despite these considerations, we have contributed to the growing body of literature on artificial intelligence integration in nursing education. By examining nursing students’ readiness, acceptance, and intention to use artificial intelligence-based healthcare technologies across multiple universities, we have provided insights into students’ preparedness for emerging digital healthcare environments in Saudi Arabia. Additionally, the inclusion of multivariable analysis allowed identification of independent predictors of readiness and acceptance beyond simple bivariate relationships. 6. Limitations and future research The study is limited by its cross-sectional design, convenience sampling, and reliance on self-reported data, which may restrict generalizability and introduce bias. Because students were recruited from 10 universities, unmodeled clustering at the institutional level may have influenced standard errors and statistical inference. The absence of multilevel modeling or other cluster-adjusted approaches limited the ability to account for possible between-university differences. Researchers should employ longitudinal and experimental designs to better understand changes over time and evaluate the effectiveness of artificial intelligence-based educational interventions. Further studies are also needed to explore the nature, quality, and educational relevance of prior artificial intelligence exposure. 7. Conclusion Participating nursing students demonstrated conceptual and ethical preparedness for artificial intelligence integration but reported gaps in technical competence. Academic progression was associated with higher readiness and acceptance, while prior exposure was also associated with more favorable readiness and acceptance outcomes. We suggest that Saudi nursing students may have lower technical skills and practical competence compared with their conceptual, ethical, and attitudinal readiness for artificial intelligence, highlighting the potential value of further attention to these areas within artificial intelligence education. CRediT authorship contribution statement Ali D. Abousoliman: Writing – review & editing, Writing – original draft, Visualization, Funding acquisition, Data curation, Conceptualization. Mohamed Gamal El-sehrawy: Resources, Methodology, Investigation. Heba Gamal Elgamal: Visualization, Software, Resources, Project administration. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgment The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number PSAU/2025/03/34203. Contributor Information Ali D. Abousoliman, Email: Alidaif81@gmail.com. Mohamed Gamal El-sehrawy, Email: m.elsehrawy@psau.edu.sa. References Al Omari O., Alshammari M., Al Jabri W., et al. Demographic factors, knowledge, attitude and perception and their association with nursing students’ intention to use artificial intelligence (AI): a multicentre survey across 10 Arab countries. BMC Med. Educ. 2024;24:1456. doi: 10.1186/s12909-024-06452-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Al Shahrani A. 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