Attitudes Toward Artificial Intelligence: Their Association with Creative Self- Efficacy and Problem-Solving Ability in Nursing Interns

Attitudes Toward Artificial Intelligence: Their Association with Creative Self- Efficacy and Problem-Solving Ability in Nursing Interns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Attitudes Toward Artificial Intelligence: Their Association with Creative Self- Efficacy and Problem-Solving Ability in Nursing Interns Mengjia Yi, Yanmei Gan, Tingting Liao, Lan Luo, Yunxu Huang, Gaoye Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9017947/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Aim To determine the levels of attitudes toward artificial intelligence（AI）, creative self-efficacy, and problem-solving ability among nursing interns in Guangxi, China, and to explore the relationships among these variables as well as the key factors influencing attitudes toward artificial intelligence. Design A cross-sectional survey design was conducted. Methods Data were collected from 259 nursing interns from seven hospitals using an online questionnaire assessing sociodemographic characteristics, AI attitudes, creative self-efficacy, and problem-solving ability. Correlation and multiple linear regression analyses were performed. The study followed ethical principles and STROBE reporting guidelines. Data Sources Survey data were collected between July and August 2025. Results The mean score for nursing interns’ attitudes toward artificial intelligence (AIA) was 47.70 ± 8.75, the mean score for creative self-efficacy (CSE) was 28.56 ± 6.20, and the mean score for problem-solving ability (PSA) was 79.83 ± 17.53.Spearman correlation analysis showed that AIA was positively correlated with PSA ( p < 0.05) and CSE ( p < 0.05), and that CSE was positively correlated with PSA ( p < 0.05).Multiple linear regression analysis further indicated that educational program, CSE, and PSA were significant factors influencing AIA. Conclusion Positive attitudes toward artificial intelligence were associated with higher creative self-efficacy and problem-solving ability. Integrating AI-related training into nursing education may support the development of professional competencies in nursing students. Intern nursing students AI attitudes Creative self-efficacy Problem-solving ability Impact / Implications for Professional Practice and Patient Care This study highlights nursing interns’ perceptions of artificial intelligence in clinical practice. The findings indicate that fostering positive attitudes toward artificial intelligence may enhance creative self-efficacy and problem-solving ability. Integrating artificial intelligence–related education and training into nursing curricula may better prepare future nurses for technology-enhanced clinical environments and support improvements in professional practice and patient care. Introduction Artificial Intelligence (AI) is increasingly being integrated into healthcare systems and has demonstrated considerable potential in supporting clinical decision-making, enabling real-time patient monitoring, and optimizing nursing workflows [ 1 – 5 ] . The ongoing advancement of smart hospitals and intelligent nursing technologies has increasingly exposed nurses to artificial intelligence (AI) applications in clinical practice. This growing exposure not only necessitates technical proficiency but also places new demands on nurses’ abilities to comprehend health-related information, their attitudes toward emerging technologies, and their capacity for AI-informed clinical decision-making. Nursing interns represent a critical transitional group from academic education to clinical practice [ 6 ] , during which their professional identity and core competencies are still developing. Their perceptions and attitudes toward emerging technologies are therefore particularly malleable [ 7 ] . While interns gradually engage with electronic health records, intelligent monitoring systems, and clinical decision support tools, differences in their attitudes toward artificial intelligence (AIA) may influence learning engagement, willingness to adopt technology, and participation in clinical decision-making [ 8 ] by shaping perceived usefulness, perceived ease of use, and subsequent adoption intentions. Previous studies have shown that attitudes toward technology are key psychological determinants of adoption and use behaviors [ 9 , 10 ] . The Technology Acceptance Model (TAM) further conceptualizes attitudes as a central link between technology perceptions and usage behavior [ 11 ] . Accordingly, examining nursing interns’ AIA is important for understanding technology adoption in clinical education and practice. AIA are likely intertwined with self-perceived competence. Social cognitive theory emphasizes that self-efficacy beliefs play a critical role when individuals encounter complex or novel tasks [ 12 ] . Empirical studies employing visual narrative methods, such as Photovoice, further suggest that attitudes toward technology are shaped not only by external contexts but also by individuals’ self-efficacy [ 13 ] . Creative self-efficacy (CSE), which reflects confidence in generating novel ideas and solving problems in uncertain or complex situations, is particularly relevant as AI increasingly informs nursing decision-making [ 14 ] . Similarly, problem-solving ability (PSA) is a core nursing competency, reflecting the capacity to integrate knowledge, analyze clinical problems, and make informed decisions. Its development is influenced by knowledge acquisition, practical experience, and psychological factors, including self-efficacy, motivation, and innovative awareness [ 15 ] .In clinical contexts where AI tools are prevalent, AIA, CSE, and PSA may be interrelated. However, research systematically examining these relationships among nursing interns remains limited. The Study 3.1 Aim To investigate the levels of attitudes toward artificial intelligence, creative self-efficacy, and problem-solving ability among nursing interns, and to examine the associations among these variables. 3.2 Design A cross-sectional survey design was adopted Methods 4.1 Setting and sample This cross-sectional study was conducted between July and August 2025. Using convenience sampling, nursing interns from seven hospitals in Guangxi, China, were recruited. A total of 259 nursing interns, including students from junior colleges and undergraduate programs, participated in the study. 4.2 Inclusion and exclusion criteria The inclusion criteria were as follows: (1) full-time nursing students enrolled in junior college or undergraduate programs; and (2) voluntary participation with informed consent. The exclusion criteria were: (1) inability to correctly understand the questionnaire content; (2) suspension from school or non-participation in the clinical internship during the study period; and (3) absence from the internship post during data collection due to personal leave or other reasons. According to Kendall’s rule of thumb, the sample size should be five to ten times the number of independent variables [ 16 ] . In this study, the general information questionnaire comprised 14 items. The Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing included two dimensions, the Creative Self-Efficacy Scale comprised one dimension, and the Problem-Solving Ability Scale included five dimensions, resulting in a total of 22 independent variables. Based on ten times the number of independent variables and accounting for a 10% invalid response rate, the minimum required sample size was calculated to be 245. Ultimately, 259 valid questionnaires were obtained. 4.3 Instruments General information questionnaire A self-designed general information questionnaire was developed in accordance with a literature review and the objectives of the study. The questionnaire collected data on participants’ demographic characteristics, as well as their knowledge and use of AI, including sources of AI-related information, level of familiarity, frequency of use, and participation in AI-related courses or training programs. Attitude toward the use of artificial intelligence in nursing Attitude toward the use of artificial intelligence in nursing were assessed using the Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN). The original scale was developed by Yilmaz et al. in 2024 [ 17 ] to assess clinical nurses’ use of artificial intelligence in nursing practice. The scale comprises 15 items across two dimensions (positive attitude and negative attitude) and is rated on a 5-point Likert scale, with total scores ranging from 15 to 75. The Chinese version of the scale (ASUAITIN-C) was translated and validated by Hu et al. in 2025 [ 18 ] , demonstrating a Cronbach’s α of 0.785 and a test–retest reliability of 0.91. In the present study, the Cronbach’s α coefficient was 0.862. Confirmatory factor analysis indicated acceptable structural validity (χ²/df = 3.10, GFI = 0.883, RMSEA = 0.090), supporting the suitability of the ASUAITIN-C for use among nursing interns. Creative self-efficacy Creative self-efficacy was measured using the Creative Self-Efficacy Scale developed by Gu et al. [ 19 ] , which was originally designed for employee populations and later adapted for use in nursing groups by Ju et al [ 20 ] . The scale consists of eight items rated on a 5-point Likert scale, with total scores ranging from 8 to 40. Higher scores indicate greater creative self-efficacy. In previous studies, the scale demonstrated good internal consistency, with a Cronbach’s α of 0.932. Problem-solving ability Problem-solving ability was assessed using the Chinese Version of the Social Problem-Solving Inventory (C-SPSI). The original scale was developed by Siu et al. in 2005 [ 21 ] and later translated and simplified by Wang in 2010 for use among nursing interns [ 22 ] . The scale consists of 25 items across five dimensions: Avoidance Style (AS), Impulsivity/Carelessness Style (ICS), Negative Problem Orientation (NPO), Positive Problem Orientation (PPO), and Rational Problem Solving (RPS). All items are rated on a 5-point Likert scale. PPO and RPS are positively scored, whereas AS, ICS, and NPO are reverse-scored and were recoded during data analysis. Total scores range from 25 to 125, with higher scores indicating stronger problem-solving ability. The scale has demonstrated good reliability, with a Cronbach’s α coefficient of 0.871 and a test–retest reliability of 0.931. 4.4 Data collection After receiving standardized training, members of the research team distributed the questionnaires through an anonymous online survey platform (Wenjuanxing) using a QR code. A standardized instruction page explained the purpose of the study and provided guidance on completing the questionnaire. All items were mandatory, and incomplete questionnaires could not be submitted. To prevent duplicate responses, each IP address and device was restricted to a single submission. Questionnaires displaying identical responses throughout, inconsistent answers, or a completion time of less than 180 seconds were excluded. A total of 261 questionnaires were collected, of which 259 were valid, resulting in an effective response rate of 99%. 4.5 Statistical analysis Data were analyzed using SPSS version 27.0. The normality of continuous variables was assessed using the Shapiro–Wilk test, and data with p > 0.05 were considered to be normally distributed. Normally distributed data are presented as mean ± standard deviation and compared using independent-samples t tests or one-way analysis of variance. Non-normally distributed data are presented as median and interquartile range and compared using non-parametric tests. Categorical variables are presented as frequencies and percentages and analyzed using chi-square tests. Pearson or Spearman correlation analyses were conducted to examine associations between continuous variables, according to data distribution. AIA in nursing was treated as the dependent variable. Variables with P < 0.05 in univariate analyses, along with theoretically relevant variables, were considered for inclusion in the multivariate model to avoid overlooking potentially important predictors. A multiple linear regression analysis was performed to identify factors independently associated with attitudes. Multicollinearity was assessed using variance inflation factors (VIFs), and all VIFs < 5 indicated acceptable collinearity. Regression assumptions—including linearity, normality of residuals, independence, and homoscedasticity—were checked and satisfied. A two-tailed P value < 0.05 was considered statistically significant. 4.6 Ethical Considerations The study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (Approval No. 2025-E0681). All participants provided informed consent before participation. Results 5.1 Levels of AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability Among Intern Nursing Students The mean score for AIA among nursing interns was 47.70 ± 8.75. The mean score for CSE was 28.56 ± 6.20, and that for PSA was 79.83 ± 17.53. Detailed results are presented in Table 1 .Normality was assessed using the Shapiro–Wilk test. The results indicated that AIA scores deviated from normal distribution (P < 0.05). Considering the relatively large sample size and the approximate symmetry of the score distribution, mean and standard deviation were retained for descriptive reporting, while non-parametric statistical methods were applied in subsequent analyses. Table 1 Current Status of AI Attitude, Creative Self-Efficacy, and Problem-Solving Ability among Nursing Interns Scale Dimension Number of items Score range Dimension score Mean(SD) Item score (Mean(SD)] AI Attitude Negative dimension 6 9 15 6–30 9–45 15–75 15.59 ± 5.62 32.11 ± 6.61 47.70 ± 8.75 2.60 ± 0.94 3.57 ± 0.73 3.18 ± 0.58 Positive dimension Total score Creative Self-Efficacy Total score 8 8–40 28.56 ± 6.20 3.57 ± 0.77 Problem-Solving Ability Avoidance style 6 6–30 20.16 ± 4.62 3.36 ± 0.77 Impulsivity/Carelessness style 4 4–20 12.30 ± 3.06 3.07 ± 0.77 Negative problem orientation 5 5–25 15.69 ± 3.83 3.14 ± 0.77 Positive problem orientation 5 5–25 16.68 ± 3.61 3.34 ± 0.72 Rational problem-solving 5 5–25 15.00 ± 4.29 3.00 ± 0.86 Total score 25 25–125 79.83 ± 17.53 3.20 ± 0.70 5.2 General Characteristics of Intern Nursing Students and Univariate Analysis of AI Attitudes A total of 259 nursing interns were included in the study, comprising 216 females and 43 males, with a mean age of 18.97 ± 1.92 years. Normality testing using the Shapiro–Wilk test indicated that AIA scores were not normally distributed; consequently, non-parametric statistical methods were applied. Univariate analysis results indicated that AIA differed significantly by age, educational program, familiarity with AI, frequency of AI product use, and participation in AI-related courses or training (all P 0.05). Detailed results are presented in Table 2 . Table 2 Univariate Analysis of AI Attitude Scores among Nursing Interns (N = 259) Variable n (%) AI Attitude Score[ M ( P 25 , P 75 )] Test Statistic Z/H value Gender Male 43(16.60) 216(83.40) 45.00(45.00,52.00) 44.00(44.00,51.00) -1.760 1) 0.078 Female Age ≤ 18 >18 145(56.00) 114(44.00) 44.00(44.00,51.00) 44.00(44.00,51.00) -7.220 1) <0.001 Ethnicity Han 201(77.60) 49(18.90) 9(3.50) 45.00(45.00,58.00) 47.00(47.00,59.00) 45.00(45.00,55.00) 2.980 2) 0.225 Zhuang Other Only child status Yes 25(9.70) 234(90.30) 45.00(45.00,51.00) 44.00(44.00,51.00) 1.750 1) 0.081 No Place of residence Rural 174(67.20) 85(32.80) 44.00(44.00,51.00) 44.00(44.00,52.00) -1.400 1) 0.163 Urban Average monthly personal expenditure 3000 Educational program Diploma program in nursing Bachelor’s program in nursing 183(70.70) 76(29.30) 44.00(44.00,51.00) 45.00(45.00,51.00) -6.290 1) <0.001 Duration of current clinical internship < 5 months 248(95.80) 11(4.20) 44.00(44.00,51.00) 44.00(44.00,60.00) -1.000 1) 0.317 ≥ 5 months Familiarity with AI-related products (e.g., ChatGPT, ERNIE Bot) Not familiar 61(23.60) 120(46.30) 56(21.60) 22(8.50) 44.00(44.00,51.00) 44.00(44.00,50.00) 45.00(45.00,53.00) 37.00(37.00,56.00) 12.090 2) 0.007 Moderately familiar Familiar Very familiar Frequency of using AI-related products Never 20(7.70) 85(32.80) 87(33.60) 51(19.70) 16(6.20) 41.00(41.00,59.00) 45.00(45.00,51.00) 44.00(44.00,50.00) 44.00(44.00,51.00) 46.00(46.00,68.00) 17.230 2) 0.002 Rarely Sometimes Often Always Participation in AI-related courses or training Yes 69(26.60) 190(73.40) 45.00(45.00,55.00) 44.00(44.00,51.00) -2.320 1) 0.020 No Sources of AI knowledge Peers or friends 59(22.80) 100(38.60) 29(11.20) 22(8.50) 49(18.90) 45.00(45.00,52.00) 45.00(45.00,50.00) 41.00(41.00,50.00) 37.00(37.00,52.00) 43.00(43.00,51.00) 7.740 2) 0.101 Media Teachers Lectures or seminars Others 1）： Z value；2） H value。 5.3 Correlations Among AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability in Intern Nursing Students Spearman correlation analysis revealed a moderate positive correlation between the positive dimension of AIA and CSE ( P < 0.05), while the negative dimension of AIA was negatively correlated with CSE ( P < 0.05). The positive dimension of AIA was significantly and positively correlated with positive problem orientation, avoidance style, and rational problem solving within PSA (all P 0.05). The negative dimension of AIA was negatively correlated with all five dimensions of PSA (all P < 0.05), indicating that stronger negative AIA was associated with greater difficulty in adopting effective problem-solving strategies. In addition, CSE was positively correlated with all PSA dimensions (all P < 0.05). Detailed results are presented in Table 3. Table 3. Correlation analysis (r) among AI attitudes, creative self-efficacy, and problem-solving ability in nursing interns Variables 1 2 3 4 5 6 7 8 1Creative Self-Efficacy 1 2Positive Dimension of AI Attitudes 0.316** 1 3Negative Dimension of AI Attitudes -0.159* -0.019 1 4Avoidant Problem-Solving Style 0.555** 0.219** -0.253** 1 5Impulsive Problem-Solving Style 0.422** 0.124* -0.307** 0.757** 1 6Negative Problem Orientation 0.360** 0.105 -0.273** 0.721** 0.852** 1 7Positive Problem Orientation 0.470** 0.265** -0.247** 0.823** 0.755** 0.821** 1 8Rational Problem-Solving 0.265** 0.129* -0.289** 0.651** 0.849** 0.837** 0.684** 1 5.4 Multivariate Regression Analysis of Factors Associated With AI Attitudes Among Intern Nursing Students The total AIA score among nursing interns was used as the dependent variable. Variables that were statistically significant in the univariate analysis were entered as independent variables in a multivariate linear regression model. The coding of categorical variables is shown in Table 4, while continuous variables were entered using their original values. Multivariate linear regression analysis indicated that educational program, CSE, and PSA were the primary factors associated with AIA among nursing interns. Detailed results are shown in Table 5. Table 4. Variable Coding Scheme Variable Coding Age ≤18=1 ＞18=2 Educational program Diploma program in nursing=1 Bachelor’s program in nursing=2 Familiarity with AI Not very familiar=1 Moderately familiar=2 Relatively familiar=3 Very familiar=4 Frequency of AI product use Never=1 Rarely=2 Sometimes=3 Often=4 Always=5 Participation in AI-related courses or training Yes=1 No=2 Table 5. Multiple Regression Analysis of Factors Influencing AI Attitudes in Nursing Interns Predictor B SE b ， T P VIF Educational program 6.602 1.166 0.350 5.660 0.000 1.220 Creative Self-Efficacy 0.418 0.093 0.302 4.514 0.000 1.424 Problem-Solving Ability 0.360 0.119 0.199 3.026 0.003 1.373 Discussion 6.1 Levels of AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability Among Intern Nursing Students The results of this study indicate that nursing interns’ attitudes toward artificial intelligence generally reflect a cautious yet receptive pattern, suggesting that they recognize the potential value of AI in nursing while still exhibit caution and some anxiety regarding technology adaptation. On the one hand, interns are becoming increasingly aware of AI applications, including intelligent monitoring, clinical decision support, and automated nursing documentation. On the other hand, interns’ limited clinical experience and the absence of systematically integrated AI content in the curriculum may restrict their understanding of and trust in AI functions, leading to a certain degree of technical anxiety and uncertainty. These findings align with those reported by Salameh et al. [ 9 ] among Palestinian nursing students. Previous research has also shown that although widespread AI adoption enhances nurses’ recognition of its clinical value [ 23 ] , technology-related anxiety and trust issues remain prevalent. In addition, this study found that nursing interns’ creative self-efficacy remains under development, consistent with the findings reported by Ju et al. [ 20 ] among nurses in tertiary hospitals. This suggests that while interns value creativity, they are still in a transitional stage between theoretical knowledge and practical application. Current nursing education emphasizes the integration of evidence-based practice and clinical decision-making [ 24 ] , promoting scientific decision-making and patient safety through standardized, evidence-guided training. While this structured approach safeguards safe and reliable practice, it may inadvertently limit interns’ creativity. In particular, differences in institutional hierarchy and teaching systems between educational institutions and clinical placements often lead preceptors to prioritize procedural compliance over innovative thinking, thereby constraining the development of CSE. Furthermore, nursing interns’ PSA results indicate that they are in the process of developing systematic thinking and enhancing rational analysis in complex clinical situations. This finding aligns with Wang’s study [ 22 ] , which highlighted that PSA depends not only on foundational nursing knowledge but also on the integration of theory with clinical context, multidimensional analysis, and the selection of optimal interventions. The development of PSA requires repeated practice and reflective learning. Through sustained exposure to complex scenarios and simulated tasks, interns can progressively enhance their analytical depth and decision-making accuracy. These findings suggest that, although interns possess a foundation in systematic thinking and rational analysis, they may still face limitations in flexibility and comprehensiveness when addressing complex nursing situations. Overall, this study indicates that nursing interns are still developing in terms of AIA, CSE, and PSA. The observed interrelationships among these variables suggest that educational interventions should target their multidimensional and coordinated development. Specifically, structured AI curricula combined with practical training can enhance interns’ familiarity with and trust in AI, thereby alleviating technology-related anxiety. Simultaneously, integrating innovative thinking exercises with clinical problem-solving-oriented instruction can enhance CSE and PSA, enabling interns not only to understand and accept AI technologies but also to proactively integrate them into practice and optimize care strategies in complex nursing scenarios, thereby improving overall nursing competence. 6.2 Effects of Educational Level, Creative Self-Efficacy, and Problem-Solving Ability on AI Attitudes Multiple regression analysis indicated that educational program, CSE, and PSA are the primary factors influencing nursing interns AIA. This suggests that the development of AIA is not solely determined by background or skills, but rather reflects the combined influence of multiple cognitive and psychological factors. Among these factors, the impact of educational program was particularly notable. Interns enrolled in bachelor’s programs demonstrated more positive AIA than those in diploma programs, which may be attributable not merely to the program itself, but also to differences in the cognitive resources and learning contexts provided at each educational stage. Bachelor’s programs typically introduce courses such as evidence-based nursing and research methodology at an earlier stage [ 25 ] , and through structured theoretical instruction and critical thinking training, students are better prepared to understand AI’s role in clinical decision support. In contrast, diploma programs focus primarily on technical skills, offering limited systematic exposure to AI-related knowledge and applications, which may contribute to interns’ unfamiliarity with and psychological resistance to new technologies. Thus, the educational program may influence differences in AI attitudes through variations in cognitive exposure and learning contexts. Furthermore, CSE positively predicted AIA, indicating that, in the context of emerging technologies, interns with greater confidence in their creativity and problem-solving abilities are more likely to develop positive AIA. This finding aligns with Bandura’s self-efficacy theory [ 26 ] , which posits that efficacy beliefs influence attitudes and behavioral intentions, partially explaining individual differences in the development of AIA. Similarly, PSA was also an important determinant of AIA. Interns with higher PSA are better able to evaluate the advantages and limitations of emerging technologies such as AI, reducing the likelihood of perceiving them solely as risks or threats, and consequently exhibiting greater acceptance [ 27 ] . Overall, the educational program, CSE, and PSA may collectively constitute the key cognitive and psychological foundations shaping nursing interns’ AIA. When integrating AI into nursing education, it is important not only to emphasize knowledge and skill acquisition but also to enhance interns’ CSE and PSA through contextualized instruction and problem-based learning. This approach can help interns progressively develop understanding, trust, and appropriate application of AI in real clinical settings, thereby enhancing their acceptance of the technology. 6.3 Associations Between AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability This study found that AIA were positively correlated with both CSE and PSA, suggesting that a positive AIA may be associated with higher levels of innovative confidence and enhanced rational thinking skills. Specifically, positive AIA were positively correlated with all five dimensions of PSA (avoidance style, impulsive style, negative problem orientation, positive problem orientation, and rational problem solving; r = 0.105–0.265, P < 0.05), whereas negative AIA were negatively correlated with the same dimensions (r = -0.247 to -0.307, P < 0.01). Among these, the correlations of positive AIA with positive problem orientation (r = 0.265) and avoidance style (r = 0.219) were relatively stronger. These findings indicate that interns with positive AIA are less likely to exhibit avoidance tendencies when confronting problems, whereas negative AIA are more strongly associated with impulsive styles, suggesting a greater propensity for irrational decision-making in complex situations. This is consistent with the findings of Ayman et al. [ 28 ] , in which students with positive AIA demonstrated higher diagnostic accuracy and more systematic thinking in simulated case analyses. AI technologies may provide interns with novel cognitive support, facilitating more rational analytical frameworks in clinical decision-making. Similarly, Salem et al. [ 29 ] found that when individuals harbor doubts about AI reliability, their decision-making processes are more susceptible to increased cognitive load. In addition, positive AIA were positively correlated with CSE (r = 0.316, P < 0.01), whereas negative AIA showed a negative correlation, suggesting that AIA may be closely linked to interns’ levels of innovative self-belief. Interns with higher CSE may be more inclined to approach new technologies with openness and exploratory attitudes, whereas negative attitudes may undermine their innovative confidence and learning initiative. Taken together, these findings suggest a close association among AIA, CSE, and PSA in nursing interns. Positive AIA may foster open and exploratory thinking, thereby promoting rational decision-making and innovative behaviors, whereas negative attitudes may induce cognitive defensiveness and learning avoidance, ultimately limiting the realization of individual potential. Therefore, nurse educators should incorporate AI literacy, innovative thinking, and problem-solving strategies into the curriculum. Through AI-based scenario simulations, case discussions, and interdisciplinary project-based learning, educators can support interns in developing positive, rational, and critical attitudes toward AI, thereby laying a foundation for professional adaptation in future smart healthcare environments. Conclusion AIA, CSE, and PSA demonstrated generally favorable score distributions among intern nursing students, with significant positive correlations observed among the three variables. The educational program, CSE, and PSA were identified as the primary factors associated with AIA in this population. Limitation This study employed a cross-sectional design, which limits the ability to infer causal relationships among variables and permits only the identification of associations. In addition, all data were collected via self-reported questionnaires, which may introduce common method variance (CMV) and response bias, potentially influencing the observed relationships among variables. Although statistical analyses were performed to mitigate this risk, the influence of CMV cannot be entirely ruled out. Furthermore, convenience sampling was employed, and the sample drawn from seven hospitals within a single geographic region (Guangxi Province), which may limit both the representativeness of the sample and the generalizability of the findings to intern nursing students in other regions. Future studies are recommended to adopt longitudinal or experimental designs, utilize multiple data sources or objective measures to minimize common method bias, and conduct multicenter investigations with larger and more diverse samples to enhance the robustness and generalizability of the findings. Declarations Conflict of Interest The authors declare no conflict of interest. Funding This work was supported by the Undergraduate Education and Teaching Reform Project of Guangxi Medical University (Grant No. 2025XJGYC49). Funding This work was supported by the Undergraduate Education and Teaching Reform Project of Guangxi Medical University (Grant No. 2025XJGYC49). Author Contribution Mengjia Yi and Gaoye Li conceived and designed the study.Mengjia Yi, Gaoye Li, Tingting Liao and Lan Luo collected the data.Mengjia Yi performed the statistical analysis and drafted the manuscript.Yanmei Gan supervised the study and critically revised the manuscript.Yunxu Huang contributed to interpretation of data and manuscript revision.All authors approved the final manuscript. Acknowledgements The authors thank all nursing interns who participated in this study and the participating hospitals for their support. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Robert N. How artificial intelligence is changing nursing[J]. Nurs Manage. 2019;50(9):30–9. Ouanes K, Farhah N. Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery[J]. J Med Syst. 2024;48(1):74. 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Gu YD, Peng JS. The influence of organizational innovation climate on employees’ innovative behavior: The mediating role of creative self-efficacy[J]. Nankai Bus Rev. 2010;13(1):30–41. (in Chinese). Ju YX, Zhao XM. Status and influencing factors of creative self-efficacy among clinical nurses in tertiary general hospitals. PLA J Nurs. 2020;37(2):28–31. Siu AM, Shek DT. The Chinese version of the social problem-solving inventory: some initial results on reliability and validity[J]. J Clin Psychol. 2005;61(3):347–60. Wang W. A study on the status of self-directed learning ability and problem-solving ability among nursing interns [dissertation]. Shanghai: Fudan University; 2010. Ramadan OME, Alruwaili MM, Alruwaili AN, et al. Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives[J]. BMC Nurs. 2024;23(1):891. Gong Q, Xia T, Du J, et al. Correlation between evidence-based practice ability and clinical decision-making ability among undergraduate nursing interns. Gen Pract Nurs. 2023;21(5):699–702. (in Chinese). Zhou Q, Xu YP, Cai YC. Multidimensional analysis of artificial intelligence literacy and its influencing factors among university students. J Libr Inform Sci. 2024;41(3):38–48. (in Chinese). Bandura A. Social cognitive theory: an agentic perspective[J]. Annu Rev Psychol. 2001;52:1–26. Yan Q, Wang SR. Factors influencing social loafing tendencies in human–AI collaborative creative tasks. J Psychol Sci. 2025;15(2):123–36. (in Chinese). El-Ashry AM, Al SN, AlOtaibi NG, et al. The Impact of Artificial Intelligence Attitudes and Acceptance on Critical Thinking Motivation among Nursing Students in Saudi Arabia[J]. SAGE Open Nurs. 2025;11:23779608251369564. Salem G, El-Gazar HE, Mahdy AY, et al. Nursing Students' Personality Traits and Their Attitude toward Artificial Intelligence: A Multicenter Cross-Sectional Study[J]. J Nurs Manag. 2024;2024:6992824. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9017947","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613835135,"identity":"5e3e3619-4309-4de7-b120-c7305776bf0c","order_by":0,"name":"Mengjia Yi","email":"","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengjia","middleName":"","lastName":"Yi","suffix":""},{"id":613835136,"identity":"6dddaae1-399d-4165-95b8-fbd50fbb640d","order_by":1,"name":"Yanmei Gan","email":"","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanmei","middleName":"","lastName":"Gan","suffix":""},{"id":613835137,"identity":"5e021fc3-ca73-49ab-acd8-8ab16f748c4a","order_by":2,"name":"Tingting Liao","email":"","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Liao","suffix":""},{"id":613835138,"identity":"4de7bab9-866c-40b3-8bad-f5e737f3e3db","order_by":3,"name":"Lan Luo","email":"","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Luo","suffix":""},{"id":613835139,"identity":"b4ee25cb-60a8-4cf1-b2a5-02cb82529281","order_by":4,"name":"Yunxu Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunxu","middleName":"","lastName":"Huang","suffix":""},{"id":613835140,"identity":"4886e93f-7dd3-45b6-8c3c-92489af853b5","order_by":5,"name":"Gaoye Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYLCCBwwMcmzszQdI0JLAwGDMx3MsgTQtifMkchSIUy0fkXvsQcKvw+ltDDkMDD8qthHWYnjmXLpBYt/h3DaGswcYe87cJkJLe4+ZRGIPUAtjXwIzYxsxWpp5wFrS2Zh5DIjTIs8OtCXhx+EENjZitRjwnAHa0pBu2MbDlnCQKL/Iz8gxk/jwx1pefv7jgw9+VBBjywEgwdjWDOYcIKweZEsDiPxTR5TiUTAKRsEoGKEAAPRFPGVr3giFAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Gaoye","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-03 08:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9017947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9017947/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105755909,"identity":"982a2a51-e1ff-4aa2-a451-774f4053a328","added_by":"auto","created_at":"2026-03-30 16:32:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1236391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9017947/v1/b4296771-2aca-4871-ac01-2de1b6c88bf0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Attitudes Toward Artificial Intelligence: Their Association with Creative Self- Efficacy and Problem-Solving Ability in Nursing Interns","fulltext":[{"header":"Impact / Implications for Professional Practice and Patient Care","content":"\u003cp\u003eThis study highlights nursing interns\u0026rsquo; perceptions of artificial intelligence in clinical practice. The findings indicate that fostering positive attitudes toward artificial intelligence may enhance creative self-efficacy and problem-solving ability. Integrating artificial intelligence\u0026ndash;related education and training into nursing curricula may better prepare future nurses for technology-enhanced clinical environments and support improvements in professional practice and patient care.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) is increasingly being integrated into healthcare systems and has demonstrated considerable potential in supporting clinical decision-making, enabling real-time patient monitoring, and optimizing nursing workflows\u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The ongoing advancement of smart hospitals and intelligent nursing technologies has increasingly exposed nurses to artificial intelligence (AI) applications in clinical practice. This growing exposure not only necessitates technical proficiency but also places new demands on nurses\u0026rsquo; abilities to comprehend health-related information, their attitudes toward emerging technologies, and their capacity for AI-informed clinical decision-making.\u003c/p\u003e \u003cp\u003eNursing interns represent a critical transitional group from academic education to clinical practice\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, during which their professional identity and core competencies are still developing. Their perceptions and attitudes toward emerging technologies are therefore particularly malleable\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. While interns gradually engage with electronic health records, intelligent monitoring systems, and clinical decision support tools, differences in their attitudes toward artificial intelligence (AIA) may influence learning engagement, willingness to adopt technology, and participation in clinical decision-making\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003eby shaping perceived usefulness, perceived ease of use, and subsequent adoption intentions.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that attitudes toward technology are key psychological determinants of adoption and use behaviors\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The Technology Acceptance Model (TAM) further conceptualizes attitudes as a central link between technology perceptions and usage behavior\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Accordingly, examining nursing interns\u0026rsquo; AIA is important for understanding technology adoption in clinical education and practice. AIA are likely intertwined with self-perceived competence. Social cognitive theory emphasizes that self-efficacy beliefs play a critical role when individuals encounter complex or novel tasks\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Empirical studies employing visual narrative methods, such as Photovoice, further suggest that attitudes toward technology are shaped not only by external contexts but also by individuals\u0026rsquo; self-efficacy\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Creative self-efficacy (CSE), which reflects confidence in generating novel ideas and solving problems in uncertain or complex situations, is particularly relevant as AI increasingly informs nursing decision-making\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Similarly, problem-solving ability (PSA) is a core nursing competency, reflecting the capacity to integrate knowledge, analyze clinical problems, and make informed decisions. Its development is influenced by knowledge acquisition, practical experience, and psychological factors, including self-efficacy, motivation, and innovative awareness\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.In clinical contexts where AI tools are prevalent, AIA, CSE, and PSA may be interrelated. However, research systematically examining these relationships among nursing interns remains limited.\u003c/p\u003e\n\u003ch3\u003eThe Study\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Aim\u003c/h2\u003e \u003cp\u003eTo investigate the levels of attitudes toward artificial intelligence, creative self-efficacy, and problem-solving ability among nursing interns, and to examine the associations among these variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Design\u003c/h2\u003e \u003cp\u003eA cross-sectional survey design was adopted\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Setting and sample\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted between July and August 2025. Using convenience sampling, nursing interns from seven hospitals in Guangxi, China, were recruited. A total of 259 nursing interns, including students from junior colleges and undergraduate programs, participated in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) full-time nursing students enrolled in junior college or undergraduate programs; and (2) voluntary participation with informed consent. The exclusion criteria were: (1) inability to correctly understand the questionnaire content; (2) suspension from school or non-participation in the clinical internship during the study period; and (3) absence from the internship post during data collection due to personal leave or other reasons.\u003c/p\u003e \u003cp\u003eAccording to Kendall\u0026rsquo;s rule of thumb, the sample size should be five to ten times the number of independent variables\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In this study, the general information questionnaire comprised 14 items. The Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing included two dimensions, the Creative Self-Efficacy Scale comprised one dimension, and the Problem-Solving Ability Scale included five dimensions, resulting in a total of 22 independent variables. Based on ten times the number of independent variables and accounting for a 10% invalid response rate, the minimum required sample size was calculated to be 245. Ultimately, 259 valid questionnaires were obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Instruments\u003c/h2\u003e \u003cp\u003e \u003cb\u003eGeneral information questionnaire\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA self-designed general information questionnaire was developed in accordance with a literature review and the objectives of the study. The questionnaire collected data on participants\u0026rsquo; demographic characteristics, as well as their knowledge and use of AI, including sources of AI-related information, level of familiarity, frequency of use, and participation in AI-related courses or training programs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAttitude toward the use of artificial intelligence in nursing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAttitude toward the use of artificial intelligence in nursing were assessed using the Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN). The original scale was developed by Yilmaz et al. in 2024\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e to assess clinical nurses\u0026rsquo; use of artificial intelligence in nursing practice. The scale comprises 15 items across two dimensions (positive attitude and negative attitude) and is rated on a 5-point Likert scale, with total scores ranging from 15 to 75.\u003c/p\u003e \u003cp\u003eThe Chinese version of the scale (ASUAITIN-C) was translated and validated by Hu et al. in 2025\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, demonstrating a Cronbach\u0026rsquo;s α of 0.785 and a test\u0026ndash;retest reliability of 0.91. In the present study, the Cronbach\u0026rsquo;s α coefficient was 0.862. Confirmatory factor analysis indicated acceptable structural validity (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.10, GFI\u0026thinsp;=\u0026thinsp;0.883, RMSEA\u0026thinsp;=\u0026thinsp;0.090), supporting the suitability of the ASUAITIN-C for use among nursing interns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCreative self-efficacy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCreative self-efficacy was measured using the Creative Self-Efficacy Scale developed by Gu et al. \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which was originally designed for employee populations and later adapted for use in nursing groups by Ju et al \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The scale consists of eight items rated on a 5-point Likert scale, with total scores ranging from 8 to 40. Higher scores indicate greater creative self-efficacy. In previous studies, the scale demonstrated good internal consistency, with a Cronbach\u0026rsquo;s α of 0.932.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProblem-solving ability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProblem-solving ability was assessed using the Chinese Version of the Social Problem-Solving Inventory (C-SPSI). The original scale was developed by Siu et al. in 2005 \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e and later translated and simplified by Wang in 2010 for use among nursing interns\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The scale consists of 25 items across five dimensions: Avoidance Style (AS), Impulsivity/Carelessness Style (ICS), Negative Problem Orientation (NPO), Positive Problem Orientation (PPO), and Rational Problem Solving (RPS).\u003c/p\u003e \u003cp\u003eAll items are rated on a 5-point Likert scale. PPO and RPS are positively scored, whereas AS, ICS, and NPO are reverse-scored and were recoded during data analysis. Total scores range from 25 to 125, with higher scores indicating stronger problem-solving ability. The scale has demonstrated good reliability, with a Cronbach\u0026rsquo;s α coefficient of 0.871 and a test\u0026ndash;retest reliability of 0.931.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data collection\u003c/h2\u003e \u003cp\u003eAfter receiving standardized training, members of the research team distributed the questionnaires through an anonymous online survey platform (Wenjuanxing) using a QR code. A standardized instruction page explained the purpose of the study and provided guidance on completing the questionnaire.\u003c/p\u003e \u003cp\u003eAll items were mandatory, and incomplete questionnaires could not be submitted. To prevent duplicate responses, each IP address and device was restricted to a single submission. Questionnaires displaying identical responses throughout, inconsistent answers, or a completion time of less than 180 seconds were excluded. A total of 261 questionnaires were collected, of which 259 were valid, resulting in an effective response rate of 99%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 27.0. The normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test, and data with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were considered to be normally distributed. Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using independent-samples \u003cem\u003et\u003c/em\u003e tests or one-way analysis of variance. Non-normally distributed data are presented as median and interquartile range and compared using non-parametric tests.\u003c/p\u003e \u003cp\u003eCategorical variables are presented as frequencies and percentages and analyzed using chi-square tests. Pearson or Spearman correlation analyses were conducted to examine associations between continuous variables, according to data distribution. AIA in nursing was treated as the dependent variable. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analyses, along with theoretically relevant variables, were considered for inclusion in the multivariate model to avoid overlooking potentially important predictors. A multiple linear regression analysis was performed to identify factors independently associated with attitudes. Multicollinearity was assessed using variance inflation factors (VIFs), and all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;5 indicated acceptable collinearity. Regression assumptions\u0026mdash;including linearity, normality of residuals, independence, and homoscedasticity\u0026mdash;were checked and satisfied. A two-tailed \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (Approval No. 2025-E0681). All participants provided informed consent before participation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Levels of AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability Among Intern Nursing Students\u003c/h2\u003e \u003cp\u003eThe mean score for AIA among nursing interns was 47.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75. The mean score for CSE was 28.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.20, and that for PSA was 79.83\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53. Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.Normality was assessed using the Shapiro\u0026ndash;Wilk test. The results indicated that AIA scores deviated from normal distribution (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Considering the relatively large sample size and the approximate symmetry of the score distribution, mean and standard deviation were retained for descriptive reporting, while non-parametric statistical methods were applied in subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCurrent Status of AI Attitude, Creative Self-Efficacy, and Problem-Solving Ability among Nursing Interns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDimension score Mean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eItem score (Mean(SD)]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAI Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative dimension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6\u0026ndash;30\u003c/p\u003e \u003cp\u003e9\u0026ndash;45\u003c/p\u003e \u003cp\u003e15\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e15.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003cp\u003e32.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e \u003cp\u003e47.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive dimension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreative Self-Efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eProblem-Solving Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvoidance style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpulsivity/Carelessness style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative problem orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive problem orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRational problem-solving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026ndash;125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.83\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 General Characteristics of Intern Nursing Students and Univariate Analysis of AI Attitudes\u003c/h2\u003e \u003cp\u003eA total of 259 nursing interns were included in the study, comprising 216 females and 43 males, with a mean age of 18.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92 years. Normality testing using the Shapiro\u0026ndash;Wilk test indicated that AIA scores were not normally distributed; consequently, non-parametric statistical methods were applied. Univariate analysis results indicated that AIA differed significantly by age, educational program, familiarity with AI, frequency of AI product use, and participation in AI-related courses or training (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences in AIA were observed for the other variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Analysis of AI Attitude Scores among Nursing Interns (N\u0026thinsp;=\u0026thinsp;259)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAI Attitude Score[\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e,\u003cem\u003eP\u003c/em\u003e\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eZ/H\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e43(16.60)\u003c/p\u003e \u003cp\u003e216(83.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45.00(45.00,52.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.760\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;18\u003c/p\u003e \u003cp\u003e\u0026gt;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145(56.00)\u003c/p\u003e \u003cp\u003e114(44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.220\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e201(77.60)\u003c/p\u003e \u003cp\u003e49(18.90)\u003c/p\u003e \u003cp\u003e9(3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e45.00(45.00,58.00)\u003c/p\u003e \u003cp\u003e47.00(47.00,59.00)\u003c/p\u003e \u003cp\u003e45.00(45.00,55.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.980\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhuang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOnly child status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e25(9.70)\u003c/p\u003e \u003cp\u003e234(90.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45.00(45.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.750\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e174(67.20)\u003c/p\u003e \u003cp\u003e85(32.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.400\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAverage monthly personal expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e89(34.40)\u003c/p\u003e \u003cp\u003e160(61.80)\u003c/p\u003e \u003cp\u003e10(3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,50.00)\u003c/p\u003e \u003cp\u003e41.00(41.00,60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.920\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma program in nursing Bachelor\u0026rsquo;s program in nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183(70.70)\u003c/p\u003e \u003cp\u003e76(29.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e45.00(45.00,51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.290\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDuration of current clinical internship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e248(95.80)\u003c/p\u003e \u003cp\u003e11(4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.000\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFamiliarity with AI-related products (e.g., ChatGPT, ERNIE Bot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot familiar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e61(23.60)\u003c/p\u003e \u003cp\u003e120(46.30)\u003c/p\u003e \u003cp\u003e56(21.60)\u003c/p\u003e \u003cp\u003e22(8.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,50.00)\u003c/p\u003e \u003cp\u003e45.00(45.00,53.00)\u003c/p\u003e \u003cp\u003e37.00(37.00,56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.090\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately familiar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamiliar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery familiar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFrequency of using AI-related products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e20(7.70)\u003c/p\u003e \u003cp\u003e85(32.80)\u003c/p\u003e \u003cp\u003e87(33.60)\u003c/p\u003e \u003cp\u003e51(19.70)\u003c/p\u003e \u003cp\u003e16(6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e41.00(41.00,59.00)\u003c/p\u003e \u003cp\u003e45.00(45.00,51.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,50.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003cp\u003e46.00(46.00,68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.230\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParticipation in AI-related courses or training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e69(26.60)\u003c/p\u003e \u003cp\u003e190(73.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45.00(45.00,55.00)\u003c/p\u003e \u003cp\u003e44.00(44.00,51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.320\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSources of AI knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeers or friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e59(22.80)\u003c/p\u003e \u003cp\u003e100(38.60)\u003c/p\u003e \u003cp\u003e29(11.20)\u003c/p\u003e \u003cp\u003e22(8.50)\u003c/p\u003e \u003cp\u003e49(18.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e45.00(45.00,52.00)\u003c/p\u003e \u003cp\u003e45.00(45.00,50.00)\u003c/p\u003e \u003cp\u003e41.00(41.00,50.00)\u003c/p\u003e \u003cp\u003e37.00(37.00,52.00)\u003c/p\u003e \u003cp\u003e43.00(43.00,51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.740\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLectures or seminars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e1）：\u003cem\u003eZ\u003c/em\u003e value；2）\u003cem\u003eH\u003c/em\u003e value。\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Correlations Among AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability in Intern Nursing Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis revealed a moderate positive correlation between the positive dimension of AIA and CSE (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while the negative dimension of AIA was negatively correlated with CSE (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The positive dimension of AIA was significantly and positively correlated with positive problem orientation, avoidance style, and rational problem solving within PSA (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), but showed no significant association with negative problem orientation (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe negative dimension of AIA was negatively correlated with all five dimensions of PSA (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), indicating that stronger negative AIA was associated with greater difficulty in adopting effective problem-solving strategies. In addition, CSE was positively correlated with all PSA dimensions (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Detailed results are presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Correlation analysis (r) among AI attitudes, creative self-efficacy, and problem-solving ability in nursing interns\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e1Creative Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e2Positive Dimension of AI Attitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.316**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e3Negative Dimension of AI Attitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.159*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e4Avoidant Problem-Solving Style\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.555**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.219**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.253**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e5Impulsive Problem-Solving Style\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.422**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.124*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.307**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.757**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e6Negative Problem Orientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.360**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.273**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.721**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.852**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e7Positive Problem Orientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.470**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.265**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.247**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.823**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.755**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.821**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e8Rational Problem-Solving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.265**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.129*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.289**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.651**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.849**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.837**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.684**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Multivariate Regression Analysis of Factors Associated With AI Attitudes Among Intern Nursing Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total AIA score among nursing interns was used as the dependent variable. Variables that were statistically significant in the univariate analysis were entered as independent variables in a multivariate linear regression model. The coding of categorical variables is shown in Table 4, while continuous variables were entered using their original values.\u003c/p\u003e\n\u003cp\u003eMultivariate linear regression analysis indicated that educational program, CSE, and PSA were the primary factors associated with AIA among nursing interns. Detailed results are shown in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Variable Coding Scheme\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026le;18=1 \u0026nbsp;＞18=2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eEducational program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eDiploma program in nursing=1 \u0026nbsp;Bachelor\u0026rsquo;s program in nursing=2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eFamiliarity with AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eNot very familiar=1 Moderately familiar=2 Relatively familiar=3 Very familiar=4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eFrequency of AI product use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eNever=1 Rarely=2 Sometimes=3\u003c/p\u003e\n \u003cp\u003eOften=4 Always=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eParticipation in AI-related courses or training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eYes=1 No=2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multiple Regression Analysis of Factors Influencing AI Attitudes in Nursing Interns\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003cem\u003e\u003csup\u003e，\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEducational program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCreative Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eProblem-Solving Ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Levels of AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability Among Intern Nursing Students\u003c/h2\u003e \u003cp\u003eThe results of this study indicate that nursing interns\u0026rsquo; attitudes toward artificial intelligence generally reflect a cautious yet receptive pattern, suggesting that they recognize the potential value of AI in nursing while still exhibit caution and some anxiety regarding technology adaptation. On the one hand, interns are becoming increasingly aware of AI applications, including intelligent monitoring, clinical decision support, and automated nursing documentation. On the other hand, interns\u0026rsquo; limited clinical experience and the absence of systematically integrated AI content in the curriculum may restrict their understanding of and trust in AI functions, leading to a certain degree of technical anxiety and uncertainty. These findings align with those reported by Salameh et al. \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e among Palestinian nursing students. Previous research has also shown that although widespread AI adoption enhances nurses\u0026rsquo; recognition of its clinical value\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, technology-related anxiety and trust issues remain prevalent.\u003c/p\u003e \u003cp\u003eIn addition, this study found that nursing interns\u0026rsquo; creative self-efficacy remains under development, consistent with the findings reported by Ju et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e among nurses in tertiary hospitals. This suggests that while interns value creativity, they are still in a transitional stage between theoretical knowledge and practical application. Current nursing education emphasizes the integration of evidence-based practice and clinical decision-making\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, promoting scientific decision-making and patient safety through standardized, evidence-guided training. While this structured approach safeguards safe and reliable practice, it may inadvertently limit interns\u0026rsquo; creativity. In particular, differences in institutional hierarchy and teaching systems between educational institutions and clinical placements often lead preceptors to prioritize procedural compliance over innovative thinking, thereby constraining the development of CSE. Furthermore, nursing interns\u0026rsquo; PSA results indicate that they are in the process of developing systematic thinking and enhancing rational analysis in complex clinical situations. This finding aligns with Wang\u0026rsquo;s study \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, which highlighted that PSA depends not only on foundational nursing knowledge but also on the integration of theory with clinical context, multidimensional analysis, and the selection of optimal interventions. The development of PSA requires repeated practice and reflective learning. Through sustained exposure to complex scenarios and simulated tasks, interns can progressively enhance their analytical depth and decision-making accuracy. These findings suggest that, although interns possess a foundation in systematic thinking and rational analysis, they may still face limitations in flexibility and comprehensiveness when addressing complex nursing situations.\u003c/p\u003e \u003cp\u003eOverall, this study indicates that nursing interns are still developing in terms of AIA, CSE, and PSA. The observed interrelationships among these variables suggest that educational interventions should target their multidimensional and coordinated development. Specifically, structured AI curricula combined with practical training can enhance interns\u0026rsquo; familiarity with and trust in AI, thereby alleviating technology-related anxiety. Simultaneously, integrating innovative thinking exercises with clinical problem-solving-oriented instruction can enhance CSE and PSA, enabling interns not only to understand and accept AI technologies but also to proactively integrate them into practice and optimize care strategies in complex nursing scenarios, thereby improving overall nursing competence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Effects of Educational Level, Creative Self-Efficacy, and Problem-Solving Ability on AI Attitudes\u003c/h2\u003e \u003cp\u003eMultiple regression analysis indicated that educational program, CSE, and PSA are the primary factors influencing nursing interns AIA. This suggests that the development of AIA is not solely determined by background or skills, but rather reflects the combined influence of multiple cognitive and psychological factors. Among these factors, the impact of educational program was particularly notable. Interns enrolled in bachelor\u0026rsquo;s programs demonstrated more positive AIA than those in diploma programs, which may be attributable not merely to the program itself, but also to differences in the cognitive resources and learning contexts provided at each educational stage.\u003c/p\u003e \u003cp\u003eBachelor\u0026rsquo;s programs typically introduce courses such as evidence-based nursing and research methodology at an earlier stage \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, and through structured theoretical instruction and critical thinking training, students are better prepared to understand AI\u0026rsquo;s role in clinical decision support. In contrast, diploma programs focus primarily on technical skills, offering limited systematic exposure to AI-related knowledge and applications, which may contribute to interns\u0026rsquo; unfamiliarity with and psychological resistance to new technologies.\u003c/p\u003e \u003cp\u003eThus, the educational program may influence differences in AI attitudes through variations in cognitive exposure and learning contexts. Furthermore, CSE positively predicted AIA, indicating that, in the context of emerging technologies, interns with greater confidence in their creativity and problem-solving abilities are more likely to develop positive AIA. This finding aligns with Bandura\u0026rsquo;s self-efficacy theory \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, which posits that efficacy beliefs influence attitudes and behavioral intentions, partially explaining individual differences in the development of AIA.\u003c/p\u003e \u003cp\u003eSimilarly, PSA was also an important determinant of AIA. Interns with higher PSA are better able to evaluate the advantages and limitations of emerging technologies such as AI, reducing the likelihood of perceiving them solely as risks or threats, and consequently exhibiting greater acceptance \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Overall, the educational program, CSE, and PSA may collectively constitute the key cognitive and psychological foundations shaping nursing interns\u0026rsquo; AIA. When integrating AI into nursing education, it is important not only to emphasize knowledge and skill acquisition but also to enhance interns\u0026rsquo; CSE and PSA through contextualized instruction and problem-based learning. This approach can help interns progressively develop understanding, trust, and appropriate application of AI in real clinical settings, thereby enhancing their acceptance of the technology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Associations Between AI Attitudes, Creative Self-Efficacy, and Problem-Solving Ability\u003c/h2\u003e \u003cp\u003eThis study found that AIA were positively correlated with both CSE and PSA, suggesting that a positive AIA may be associated with higher levels of innovative confidence and enhanced rational thinking skills. Specifically, positive AIA were positively correlated with all five dimensions of PSA (avoidance style, impulsive style, negative problem orientation, positive problem orientation, and rational problem solving; r\u0026thinsp;=\u0026thinsp;0.105\u0026ndash;0.265, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas negative AIA were negatively correlated with the same dimensions (r = -0.247 to -0.307, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among these, the correlations of positive AIA with positive problem orientation (r\u0026thinsp;=\u0026thinsp;0.265) and avoidance style (r\u0026thinsp;=\u0026thinsp;0.219) were relatively stronger. These findings indicate that interns with positive AIA are less likely to exhibit avoidance tendencies when confronting problems, whereas negative AIA are more strongly associated with impulsive styles, suggesting a greater propensity for irrational decision-making in complex situations. This is consistent with the findings of Ayman et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, in which students with positive AIA demonstrated higher diagnostic accuracy and more systematic thinking in simulated case analyses. AI technologies may provide interns with novel cognitive support, facilitating more rational analytical frameworks in clinical decision-making. Similarly, Salem et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e found that when individuals harbor doubts about AI reliability, their decision-making processes are more susceptible to increased cognitive load.\u003c/p\u003e \u003cp\u003eIn addition, positive AIA were positively correlated with CSE (r\u0026thinsp;=\u0026thinsp;0.316, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas negative AIA showed a negative correlation, suggesting that AIA may be closely linked to interns\u0026rsquo; levels of innovative self-belief.\u003c/p\u003e \u003cp\u003eInterns with higher CSE may be more inclined to approach new technologies with openness and exploratory attitudes, whereas negative attitudes may undermine their innovative confidence and learning initiative. Taken together, these findings suggest a close association among AIA, CSE, and PSA in nursing interns. Positive AIA may foster open and exploratory thinking, thereby promoting rational decision-making and innovative behaviors, whereas negative attitudes may induce cognitive defensiveness and learning avoidance, ultimately limiting the realization of individual potential. Therefore, nurse educators should incorporate AI literacy, innovative thinking, and problem-solving strategies into the curriculum. Through AI-based scenario simulations, case discussions, and interdisciplinary project-based learning, educators can support interns in developing positive, rational, and critical attitudes toward AI, thereby laying a foundation for professional adaptation in future smart healthcare environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAIA, CSE, and PSA demonstrated generally favorable score distributions among intern nursing students, with significant positive correlations observed among the three variables. The educational program, CSE, and PSA were identified as the primary factors associated with AIA in this population.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study employed a cross-sectional design, which limits the ability to infer causal relationships among variables and permits only the identification of associations. In addition, all data were collected via self-reported questionnaires, which may introduce common method variance (CMV) and response bias, potentially influencing the observed relationships among variables. Although statistical analyses were performed to mitigate this risk, the influence of CMV cannot be entirely ruled out. Furthermore, convenience sampling was employed, and the sample drawn from seven hospitals within a single geographic region (Guangxi Province), which may limit both the representativeness of the sample and the generalizability of the findings to intern nursing students in other regions. Future studies are recommended to adopt longitudinal or experimental designs, utilize multiple data sources or objective measures to minimize common method bias, and conduct multicenter investigations with larger and more diverse samples to enhance the robustness and generalizability of the findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Undergraduate Education and Teaching Reform Project of Guangxi Medical University (Grant No. 2025XJGYC49).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Undergraduate Education and Teaching Reform Project of Guangxi Medical University (Grant No. 2025XJGYC49).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMengjia Yi and Gaoye Li conceived and designed the study.Mengjia Yi, Gaoye Li, Tingting Liao and Lan Luo collected the data.Mengjia Yi performed the statistical analysis and drafted the manuscript.Yanmei Gan supervised the study and critically revised the manuscript.Yunxu Huang contributed to interpretation of data and manuscript revision.All authors approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank all nursing interns who participated in this study and the participating hospitals for their support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRobert N. 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The Impact of Artificial Intelligence Attitudes and Acceptance on Critical Thinking Motivation among Nursing Students in Saudi Arabia[J]. SAGE Open Nurs. 2025;11:23779608251369564.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalem G, El-Gazar HE, Mahdy AY, et al. Nursing Students' Personality Traits and Their Attitude toward Artificial Intelligence: A Multicenter Cross-Sectional Study[J]. J Nurs Manag. 2024;2024:6992824.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intern nursing students, AI attitudes, Creative self-efficacy, Problem-solving ability","lastPublishedDoi":"10.21203/rs.3.rs-9017947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9017947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the levels of attitudes toward artificial intelligence（AI）, creative self-efficacy, and problem-solving ability among nursing interns in Guangxi, China, and to explore the relationships among these variables as well as the key factors influencing attitudes toward artificial intelligence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey design was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from 259 nursing interns from seven hospitals using an online questionnaire assessing sociodemographic characteristics, AI attitudes, creative self-efficacy, and problem-solving ability. Correlation and multiple linear regression analyses were performed. The study followed ethical principles and STROBE reporting guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvey data were collected between July and August 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean score for nursing interns’ attitudes toward artificial intelligence (AIA) was 47.70 ± 8.75, the mean score for creative self-efficacy (CSE) was 28.56 ± 6.20, and the mean score for problem-solving ability (PSA) was 79.83 ± 17.53.Spearman \u0026nbsp;correlation analysis showed that AIA was positively correlated with PSA (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) and CSE (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and that CSE was positively correlated with PSA (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).Multiple linear regression analysis further indicated that educational program, CSE, and PSA were significant factors influencing AIA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePositive attitudes toward artificial intelligence were associated with higher creative self-efficacy and problem-solving ability. Integrating AI-related training into nursing education may support the development of professional competencies in nursing students.\u003c/p\u003e","manuscriptTitle":"Attitudes Toward Artificial Intelligence: Their Association with Creative Self- Efficacy and Problem-Solving Ability in Nursing Interns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 16:24:02","doi":"10.21203/rs.3.rs-9017947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T06:45:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T08:31:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283532379341650735716569138749710216988","date":"2026-03-28T17:35:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T16:50:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164662404218960413914842582069710028745","date":"2026-03-26T19:32:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T16:07:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T09:22:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-07T02:50:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T02:50:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2026-03-03T08:21:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d06bc86-0853-4e99-9541-0010080c9bc7","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T08:25:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 16:24:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9017947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9017947","identity":"rs-9017947","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}