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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Nurs . 2026 Mar 11;25:376. doi: 10.1186/s12912-026-04526-x Search in PMC Search in PubMed View in NLM Catalog Add to search Artificial intelligence literacy and related factors among nursing undergraduates in Northeast China Dong Chen Dong Chen 1 Department of Nursing, Heilongjiang Nursing College, Harbin, Heilongjiang Province China Find articles by Dong Chen 1 , Xinjing Zhang Xinjing Zhang 2 Emergency Intensive Care Unit, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong Province China Find articles by Xinjing Zhang 2 , Shasha Zhang Shasha Zhang 3 Dermatology and Medical Cosmetology Treatment Center Dermatology DEPT, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong Province China Find articles by Shasha Zhang 3 , Yanyan Pan Yanyan Pan 1 Department of Nursing, Heilongjiang Nursing College, Harbin, Heilongjiang Province China Find articles by Yanyan Pan 1 , Chongkuan Zhu Chongkuan Zhu 4 Intensive Care Unit One, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong Province China Find articles by Chongkuan Zhu 4 , Lei Wang Lei Wang 5 School of Nursing, Dalian Medical University, Dalian, Liaoning Province China Find articles by Lei Wang 5, ✉ Author information Article notes Copyright and License information 1 Department of Nursing, Heilongjiang Nursing College, Harbin, Heilongjiang Province China 2 Emergency Intensive Care Unit, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong Province China 3 Dermatology and Medical Cosmetology Treatment Center Dermatology DEPT, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong Province China 4 Intensive Care Unit One, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong Province China 5 School of Nursing, Dalian Medical University, Dalian, Liaoning Province China ✉ Corresponding author. Received 2025 Jun 4; Accepted 2026 Mar 2; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13094127 PMID: 41814327 Abstract Background There is a potential shortage of information-based teaching resources in Northeast China, and the artificial intelligence literacy of nursing undergraduates and related factors are not clear. Methods This is a cross-sectional survey study conducted in the northeastern region of China. In this study, 546 nursing undergraduates were studied. The data was collected through a mobile app-based survey in October 2024. We assessed demographic factors and artificial intelligence literacy. Results The total score of artificial intelligence literacy of nursing undergraduates was 134.40 ± 4.38. Regression analysis showed that gender, monthly household income, attitude towards artificial intelligence, and whether or not participating in artificial intelligence-related education/training had a significant negative effect on artificial intelligence literacy ( p < .05). Conclusions It is recommended to formulate effective measures in combination with relevant factors to improve the artificial intelligence literacy of nursing undergraduates in areas with a potential shortage of information-based teaching resources and promote the digital transformation of nursing undergraduates’ future careers. Clinic trial number Not applicable. Supplementary Information The online version contains supplementary material available at 10.1186/s12912-026-04526-x. Keywords: Nursing undergraduates, Artificial intelligence, Literacy, Related factors, Northeast China, Cross-sectional study Introduction More and more studies have shown that the application of artificial intelligence (AI) in the field of nursing and digital transformation will maximize the medical benefits of patients [ 1 , 2 ]. Future nurses need to apply AI effectively and prudently to nursing practice [ 3 ]. Current nursing educators should carry out education interventions related to AI for nursing undergraduates as early as possible [ 4 ]. Due to knowledge and technical equipment, nursing undergraduates may face the problem of inequality in access to digital resources [ 5 ]. For example, students in rural or underdeveloped areas lack sufficient opportunities to participate in AI-driven learning [ 6 ]. The Northeast region is located in the northeast of China, which is remote and faces the challenges of population outflow, population aging, and economic recession [ 7 ]. It is more urgent and challenging to carry out AI-related nursing education in Northeast China. Artificial intelligence literacy, also known as AI literacy, refers to the ability to understand the basic technologies and concepts in various AI products and services [ 8 ]. UNESCO proposed in 2023 to “list AI literacy as an essential competency for students and teachers.” [ 9 ] Previous studies have investigated the relationship between AI literacy and medical AI readiness [ 10 ], academic ethics [ 11 ], and higher-order thinking skills [ 12 ] of nursing students. However, these studies failed to address the related factors of AI literacy of current nursing undergraduates. A similar study [ 13 ] was not conducted in remote areas where digital education resources are lacking. It is urgent and necessary to explore the level of AI literacy and related factors of nursing undergraduates in remote areas. Accepting AI technology is the key to improving AI literacy. According to the Technology Acceptance Model (TAM), perceived usefulness and perceived ease of use are the main determinants of explaining people’s acceptance of information technology [ 14 ]. Perceived usefulness is the driving factor. Nursing undergraduates are willing to adopt AI when they realize that AI can improve their work or life efficiency. This perception of the usefulness of AI is reflected in the attitude of nursing undergraduates toward AI. Perceived ease of use emphasizes that the technology is easy to learn and use. Participating in AI-related education/training would increase the perceived ease of use of AI by nursing undergraduates. Therefore, this study takes the technology acceptance model as the theoretical guidance, and the main research purposes are (1) to investigate the level of AI literacy of nursing undergraduates in Northeast China and (2) to analyze the related factors of AI literacy of nursing undergraduates in Northeast China. Methods and materials Study design and participants This cross-sectional study was conducted among nursing undergraduates enrolled in a medical university in Northeast China during the 2024–2025 academic year. Sample size calculation The sample size for this study was determined using a census approach, targeting all eligible nursing undergraduates enrolled in a medical university in Northeast China. The total population consisted of approximately 600 students who are studying nursing. The G*Power Windows 3.1.9.4 software determines the sample size required for a study, assuming a multiple linear regression with a moderate effect size (f ² = 0.15), an alpha of 0.05, and a power of 0.95 [ 15 ]. Considering 10 variables—including eight sociodemographic and two primary predictors—the minimum sample size was calculated to be 172 nursing undergraduates. Considering a 20% attrition rate, the minimum sample size is 207. The actual inclusion of 546 nursing undergraduates in this study is in line with the sample size range. Inclusion criteria Full-time undergraduate students studying nursing at a medical university in Northeast China. Students consented to participate in this study. Exclusion criteria Currently on academic leave. Unwilling to participate in the study for any reason. Data collection This study specially set up a survey to obtain the research data. The English version of the questionnaire can be found in Supplementary File 1 . Supplementary File 1 contains the uniform instructions, the acquisition of informed consent, and the formal survey content. In this study, the online platform ( https://www.wjx.cn ) was used to conduct the survey, and the question of whether informed consent was set as the first question and a redirect page was set. If you disagree, you can directly jump to the submission page. Before the formal investigation, 10 nursing undergraduates were randomly selected for a pretest. The pretest showed that the questions in the questionnaire were clear. The average time for 10 nursing undergraduates to fill in the questionnaire was about 8 min, which indicated that the questionnaire was convenient and easy to understand. The research objectives, inclusion and exclusion criteria, and survey links are then sent to the nursing undergraduates’ administration to conduct the survey. Set a limit on the number of questions to be answered. All questions are required. Demographic Information. This part of the questionnaire was specially designed by the author team for this study. This section gathered data on the nursing undergraduates’ gender, age, grade, place of origin, nationality, whether they are the only child, parents’ education level, monthly family income, attitude towards AI, and whether they have participated in AI-related education/training. The Artificial Intelligence Literacy Scale (AILS) was developed by Zhou Qiong [ 16 ] in 2024 to assess the level of AI literacy of China college students. Including knowledge (3 items), skills (8 items), values (6 items), and ethics (8 items), a total of 25 items in 4 dimensions. Likert 7 scores were adopted for all students. “Strongly disagree” to “strongly agree” were scored from “1” to “7” respectively, with a total score of 25 to 175 points. The higher the score, the higher the level of AI literacy of college students. AILS has undergone cultural validation. The Cronbach’s α coefficient of the four dimensions of AILS ranges from 0.775 to 0.911, and the total Cronbach’s α coefficient measured in this study is 0.865. Ethical considerations Information collected through the questionnaire will remain anonymous and confidential. The survey link will be accessible for one week, after which it will be inaccessible. No one else has the right to log in to the researcher’s personal online platform, wjx.cn and obtain information. All participants participated in the study voluntarily and gave informed consent. After the survey data obtained by the research institute is used for statistical analysis in this study, all the data is kept separately by a special person in the research group, and other people outside the research group will not be able to access it without a password. In addition, in order to increase the willingness of nursing students to participate in the survey, this survey set up a random small amount of reward (0.5 RMB ~ 1.0 RMB) that may be obtained after the survey, but not every nursing student who participates in the survey will get it. The amount of money needed for these reward is obtained from the government-approved project that supports this study. The above details have been reviewed by the ethics committee that approved this study. This study was approved by the Ethics Committee of the medical university where the researchers were located (Medical Ethics Committee of the Second Affiliated Hospital of Harbin Medical University), and the ethics approval code was KY2004-006. Statistical analysis SPSS 25.0 was used for statistical analysis. Descriptive analysis was used to summarize the data. The Kolmogorov–Smirnov test was used to determine whether the data conformed to the normal distribution. Since the data conform to an approximately normal distribution ( P > .05) and satisfy homogeneity of variance, an independent sample t-test or one-way ANOVA is employed for the single-factor analysis. Multiple linear regression analysis was used to explore the influencing factors of AI literacy of nursing undergraduates. Test level α = 0.05. Results Demographic characteristics of nursing undergraduates In this research, 584 questionnaires were given to nursing undergraduates. Out of the returned questionnaires, 546 were completed and used for statistical analysis, resulting in a response rate of 93.49%. The age range of the surveyed nursing undergraduates is 17 ~ 24 years old, and the mean age was 20.10 ± 1.19 years. Table 1 shows the frequency distribution of students based on demographic characteristics. Table 1. Frequency distribution of nursing undergraduates according to demographic characteristics ( n = 546) Variable Number Percent Variable Number Percent Gender Parents’ education level Male 72 13.2 All in primary school and below 66 12.1 Female 474 86.8 At least 1 junior/senior high school (technical secondary school) 199 36.4 Grade At least 1 junior college/bachelor degree 231 42.3 First-Year 219 40.1 Bachelor degree or above 50 9.2 Second-Year 171 31.3 Monthly household income (RMB) Junior-year 156 28.6 <5,000 117 21.4 Family location 5,000 ~ 10,000 249 45.6 Rural 232 42.5 >10,000 180 33.0 City 314 57.5 Attitude towards AI Nationality Positive 243 44.5 The Han nationality 512 93.8 Neutral 214 39.2 Non-Hannationality 34 6.2 Negative 89 16.3 Number of sibling Participated in AI-related education/training Zero 218 39.9 Yes 175 32.1 At least one 328 60.1 No 371 67.9 Open in a new tab The AI literacy score of nursing undergraduates Table 2 shows the AI literacy scores of the surveyed nursing undergraduates. Table 2. Score of AI literacy and its domains ( n = 546, ) Variable Mean ± SD Item average score Total score of AI literacy 134.40 ± 4.38 5.38 ± 0.18 knowledge 17.66 ± 1.03 5.89 ± 0.34 Skills 44.28 ± 2.18 5.54 ± 0.27 Values 32.71 ± 2.15 5.45 ± 0.36 Ethics 39.75 ± 2.80 4.97 ± 0.35 Open in a new tab Univariate analysis of AI literacy for nursing undergraduates According to the results of univariate analysis, there was no significant difference in the total score of AI literacy among nursing undergraduates of different ages, grades, places of origin, nationalities, whether they were the only child or not, and parents’ education level ( P > .05). There were statistically significant differences in the total score of AI literacy among nursing undergraduates with different genders, average monthly family incomes, attitudes towards AI, and whether they had participated in AI-related education/training ( P < .05). Table 3 shows a detailed single-factor analysis. The Bonferroni tests were used for post-hoc comparison. The results showed that there were differences in AI literacy of nursing undergraduates with monthly household income of < 5,000 RMB and > 10,000 RMB ( P < .05), and there were differences in AI literacy of nursing undergraduates with monthly household income of 5,000 ~ 10,000 RMB and > 10,000 RMB ( P < .05). There was no difference in AI literacy of nursing undergraduates with monthly household income of < 5,000 RMB and 5,000 ~ 10,000 RMB ( P > .05). There were differences in AI literacy among the three groups of nursing undergraduates with different attitudes toward AI ( P < .05). Table 3. Comparison of total score of AI literacy of nursing undergraduates with different characteristics ( n = 546, ) Factors AI literacy t/F p -value Gender t = 6.279 <0.001 Male 137.32 ± 3.79 Female 133.95 ± 4.30 Age group (year) F = 0.667 0.573 ≤ 19 134.26 ± 4.53 20 134.13 ± 4.68 21 134.73 ± 4.13 ≥ 22 134.74 ± 3.78 Grade F = 2.587 0.076 First-Year 133.90 ± 4.26 Second-Year 134.58 ± 4.68 Junior-year 134.90 ± 4.17 Family location t =-0.260 0.795 Rural 134.34 ± 4.28 City 134.44 ± 4.47 Nationality t =-1.232 0.219 The Han nationality 134.34 ± 4.42 Non-Han nationality 135.29 ± 3.83 Number of sibling t = 0.147 0.884 Zero 134.43 ± 4.34 At least one 134.38 ± 4.42 Parents’ education level F = 0.952 0.415 All in primary school and below 134.62 ± 4.38 At least 1 junior/senior high school (technical secondary school) 134.02 ± 4.51 At least 1 junior college/bachelor degree 134.70 ± 4.38 Bachelor degree or above 134.20 ± 3.90 Monthly household income (RMB) F = 19.848 <0.001 < 5,000 133.05 ± 4.10 5,000 ~ 10,000 133.90 ± 4.56 > 10,000 135.96 ± 3.86 Attitude towards AI F = 22.272 <0.001 Positive 135.59 ± 4.12 Neutral 133.93 ± 4.51 Negative 132.27 ± 3.76 Participated in AI-related education/training t = 6.848 <0.001 Yes 136.19 ± 4.19 No 133.55 ± 4.22 Open in a new tab Multiple regression analysis of AI literacy of nursing undergraduates Multiple linear regression analysis was conducted with the total score of AI literacy as the dependent variable and univariate analysis of four statistically significant variables (gender, monthly family income, attitude towards AI, and whether they had participated in AI-related education/training) as the independent variables. The assignment of independent variables is shown in Table 4 . The Variance Inflation Factor ranges from 1.031 to 1.740, indicating that there is no multicollinearity [ 17 ]. Table 4. Table of independent variable assignment Independent variable Assignment (Dummy coded) Gender Male = 1, Female = 2 Attitude towards AI Positive (0,0,0), Neutral (0,1,0), Negative (0,0,1) Monthly household income (RMB) < 5,000 (0,0,0), 5,000 ~ 10,000 (0,1,0), > 10,000 (0,0,1) Participated in AI-related education/training Yes = 1, No = 2 Open in a new tab The results of the regression analysis show that being female, having a neutral or negative attitude towards AI, and not having participated in AI-related education/training negatively affect nursing undergraduates’ AI literacy. Among these factors, the impact of a negative attitude towards AI on AI literacy can be explained by the size of the effect, followed by gender, then whether they participated in AI-related education/training, and the least impact is a neutral attitude towards AI. A monthly household income of > 10,000 RMB will positively impact nursing undergraduates’ AI literacy. The above variables explain a total variance of 42.3%. The F -test result of the regression analysis model is F = 27.225, with a P < .01, indicating that the overall regression equation is significant, demonstrating that the explanatory variables have joint explanatory power over the dependent variable. Table 5 shows more details of the regression analysis. The regression analysis results of the four dimensions of AI literacy are shown in supplementary Table 1 , supplementary Table 2 , supplementary Tables 3 , and supplementary Table 4 . Supplementary File 2 shows the regression analysis results of these four dimensions in detail. According to Supplementary File 2 , comparing the Adjusted R 2 of the regression analysis results of the four dimensions of AI literacy, it can be found that these influencing factors contribute the most to the ethical dimension (Adjusted R 2 = 0.124), and the least to the knowledge dimension, or even have almost no impact (Adjusted R 2 = 0.000). Table 5. Multiple linear regression analysis of AI literacy scores of nursing undergraduates ( n = 546) Factors B SE β t P 95%CI VIF Constant 142.618 1.134 —— 125.775 <0.001 140.390∼144.845 Gender -2.409 0.499 -0.186 -4.826 <0.001 -3.390∼-1.428 1.044 Monthly household income (RMB) (< 5,000 = contrast) > 10,000 2.213 0.464 0.237 4.771 <0.001 1.302∼3.124 1.740 Attitude towards AI (positive=contrast) Neutral -1.330 0.366 -0.148 -3.629 <0.001 -2.049∼-0.610 1.171 Negative -2.934 0.482 -0.247 -6.089 <0.001 -3.881∼-1.988 1.159 Participated in AI-related education/training -2.246 0.360 -0.239 -6.244 <0.001 -2.953∼-1.539 1.031 Open in a new tab Note R 2 = 0.463, Adjusted R 2 = 0.423; F = 27.225, P <.001 Discussion Nursing undergraduates’ AI literacy is at a moderate level The score of AI literacy of nursing undergraduates was 134.40 ± 4.38. The score was in the range of 50%~75% (100 ~ 137.5) of the total score of the Artificial Intelligence Literacy Scale, indicating that the AI literacy of nursing undergraduates was at a moderate level, which was consistent with the survey results of nursing students in other underdeveloped areas [ 18 ]. The research [ 19 ] indicates that the new generation of nursing undergraduates grew up in the context of the rapid development of the Internet, have been exposed to electronic products since childhood, and have strong learning ability and adaptability. On the other hand, the accessibility of AI products may also contribute to this result. It is undeniable that more and more AI products have emerged in China in recent years, such as Deep Seek ( https://www.deepseek.com/ ), Doubao ( https://www.doubao.com/chat/ ), etc. These AI products can be downloaded for free and used anytime and anywhere [ 20 ]. Nursing undergraduates may have a certain degree of practice in AI products in their lives outside of learning, which also promotes the level of AI literacy of nursing undergraduates in remote areas. In specific dimensions, this study found that the knowledge and skills dimension scored higher, the values dimension was moderate, while the ethics dimension scored the lowest. The results showed that nursing undergraduates had a clear understanding of AI-related products and basically had the ability to use them independently and flexibly, which was consistent with the previous research results on intern nurses [ 10 ]. The value dimension emphasizes the personalized and autonomous learning experience of students in the process of interacting with AI and the practical initiative experience of solving problems [ 21 ]. Consistent with the other survey study [ 22 ] on students, this study found that nursing undergraduates had low scores on the perception of the creative value of AI. The current AI nursing practice is also in the key stage of exploration [ 23 ], which limits the exploration of AI nursing practice by nursing undergraduates, affects the cognitive subjectivity of nursing undergraduates in the process of applying AI products, and the creative value experience is insufficient, and the introspective values of overcoming AI dependence belong to high-level thinking ability, which is difficult to obtain easily [ 21 ]. The lowest score of nursing undergraduates in the ethical dimension indicates that nursing undergraduates lack sufficient understanding of AI-related laws, privacy issues, and security issues, which is consistent with the results of previous surveys on nursing students [ 11 , 24 , 25 ]. This may be particularly important for nursing undergraduates, because engaging in AI-based nursing practice may expose nurses to the dual challenges of patient privacy security and data privacy security [ 26 ]. Promoting the fairness of AI education for nursing undergraduates in remote and relatively backward areas around the world is a common challenge facing nursing education. Our study found that values and ethics are key issues of concern, but this does not mean that the improvement of AI-related knowledge and skills of nursing undergraduates can be ignored. The fact that these Chinese undergraduates have a medium level of knowledge and skills is mainly because China can easily obtain and use various AI products. This advocates that more remote areas, including China, should implement “suitable” improvement paths, such as evidence from Indonesia suggesting the development and use of inclusive, region-specific AI education tools [ 27 ], or calling for the establishment of a global, shared, low-cost, and even offline-applicable open-source platform for nursing AI education to solve the problem of shortage of teacher resources [ 28 ]. The cultivation of AI values and ethics for nursing undergraduates is still a serious issue that needs to be widely explored. In addition to the need for more relevant research [ 11 ], it is equally important to strengthen policy support, such as UNESCO providing evidence of AI values and ethics for nursing undergraduates [ 29 ]. Analysis of factors influencing nursing undergraduates’ AI literacy Gender The results of this study show that gender is an influencing factor of AI literacy of nursing undergraduates, indicating that the AI literacy level of male nursing undergraduates is higher than that of female nursing undergraduates, which is consistent with previous research results [ 13 , 30 ]. A previous study [ 31 ] showed that the level of digital expression and digital acquisition of men was higher than that of women, showing higher digital literacy, which was conducive to increasing AI-related knowledge and skills. Another survey [ 32 ] showed that male medical students had more confidence in AI and less fear of technology. Male nursing undergraduates may be better at exploring and applying different AI products and have a certain subjective judgment on the potential value and ethical issues involved in AI products. Therefore, their AI literacy level is higher. This finding suggests that nursing educators should promote gender equality in the cultivation of the AI literacy of nursing undergraduates [ 33 ] and attach importance to the cultivation of the AI literacy level of female nursing undergraduates. For example, in the process of group teaching, the influence of peers (male) is used to enhance the overall AI literacy level of group members. When allocating AI-related digital resources, ensure that the allocation of resources is equal, and it is necessary to tilt female nursing undergraduates when necessary [ 34 ]. Monthly household income The results of the regression analysis showed that nursing undergraduates with a monthly household income of > 10,000 RMB had a higher level of AI literacy than nursing undergraduates with a monthly household income of < 5,000 RMB. This finding reminds us that the socioeconomic statuses of the family are factors affecting the AI literacy of nursing undergraduates, which is consistent with previous research results [ 35 ]. A previous study [ 36 ] pointed out that in the field of universities, college students with different socioeconomic statuses may form a digital divide due to the different access to information technology, and this difference may run through the entire university stage. Nursing undergraduates with a monthly household income of > 10,000 RMB are not restricted in accessing information technology [ 37 ]. For example, they may have been exposed to AI products earlier, used more types of AI products, used them more frequently, and have a higher acceptance and tolerance for different AI products, so their AI literacy level is higher. The findings suggest that nursing educators should provide individualized and inclusive compensatory guidance for nursing undergraduates from low-income families [ 38 ]. Nursing educators should carefully develop teaching strategies based on existing digital education resources and infrastructure. For example, the AI diagnostic assistance demonstration program that can run offline [ 39 ], the remote teaching resources constructed by the combination of virtual reality technology and AI [ 40 ], etc. Attitude towards AI The results of this study show that the AI literacy level of nursing undergraduates with a positive attitude towards AI is higher than that of those with a neutral or negative attitude, which is consistent with previous research results [ 41 – 43 ]. A positive attitude means that nursing undergraduates have a strong curiosity and learning attitude towards AI-related products [ 44 ]. They are more willing to try, explore, and learn AI-related products and perform better when using AI-related products [ 43 ]. In addition, a positive attitude will encourage nursing undergraduates to face AI-related moral and ethical issues with a more cautious and inclusive attitude. The study have shown that the Chinese nurses has an optimistic attitude of behavioral intention to adopt medical AI [ 45 ] These positive perceptions will also prompt the nurses to have certain expectations for AI governance and help nursing undergraduates establish a moral and ethical view of AI applications towards kindness. This finding reminds nursing educators that they need to improve the positive attitude of nursing undergraduates towards AI as soon as possible, especially to help nursing undergraduates in remote or economically backward areas realize the relevance of AI technology to future daily nursing work and enhance their positive experience and interaction with AI-based nursing practice [ 46 , 47 ]. Help nursing undergraduates establish a positive evaluation of AI, stimulate their intrinsic motivation for learning, and further improve the level of AI literacy. Participated in AI related education/training The results of this study show that nursing undergraduates who have participated in AI-related education/training have a higher level of AI literacy, which is consistent with previous research results [ 26 ]. Nursing undergraduates who have participated in AI-related education/training have a higher level of perceived ease of use of AI and have a technical learning background or application experience related to AI. The study have pointed out that students with a technical learning background or AI experience have higher AI literacy [ 48 ]. According to this finding, it is urgent for higher nursing colleges in remote or resource-limited areas to carry out AI-related education for nursing undergraduates [ 49 ]. These educational measures include opening professional courses such as “Nursing Informatics” and general courses such as “Fundamentals of Artificial Intelligence” [ 50 ]; carrying out various forms of education and teaching activities, such as short-term training/lectures, second classroom activities, etc [ 51 ]; and jointly formulating interdisciplinary or cross-disciplinary online learning activities with teachers of AI-related majors [ 52 ]. In addition, under the premise of limited resources, it is possible to fully consider combining AI education content with core professional courses, such as embedding AI education into existing courses such as “Fundamentals of Nursing” and “Geriatric Nursing” in a modular form and designing the “minimum effective dose” of AI education core courses [ 53 ]. This will make AI education run through the entire nursing talent training process, effectively improving the AI literacy level of nursing undergraduates in remote or AI education resource areas. Strengths and limitations First, our study focused on the level of AI literacy of nursing undergraduates in the northeast region of China, which is potentially lacking in digital teaching resources. The research results provide scientific reference for other similar areas in the world. Second, analysis results highlighted the importance of attitudes toward AI and whether or not they had participated in AI-related education/training in improving the level of AI literacy, which provided a certain reference for nursing educators to further carry out AI education or corresponding practices. However, our study also has some limitations. First, the data collection is limited to nursing undergraduates in a city in Northeast China. Second, more relevant factors such as social support and the personality of nursing undergraduates were not considered. Third, only one multiple linear regression analysis was conducted without further hierarchical regression analysis, which may not highlight the importance of a certain variable. Finally, the cross-sectional design only represents the current situation at a certain point in time, ignoring the dynamic development of AI literacy. Future studies should fully consider other relevant factors, conduct longitudinal studies, and explore more effective intervention strategies. Conclusions This study found that nursing undergraduates’ AI literacy is at a moderate level. Factors influencing nursing undergraduates’ AI literacy include gender, monthly family income, attitudes towards AI, and whether they have participated in AI-related education/training. These findings will help nursing educators establish effective educational strategies to improve the AI literacy of nursing undergraduates and help nursing undergraduates better adapt to future intelligent nursing practice. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (141.4KB, pdf) Supplementary Material 2 (132.9KB, pdf) Acknowledgements We are grateful to all nursing undergraduates from the School of Nursing, Harbin Medical University, Heilongjiang, China for participating in this study. We also thank the student administrator, Ms. Zhang Yuhuan, for her selfless help. Abbreviations AI Artificial Intelligence UNESCO United Nations Educational Scientific and Cultural Organization Author contributions All authors (DC, XJZ, SSZ, YYP, CKZ and LW) participated in the conception and design of the study. DC analyzed the data, and wrote the initial draft of the manuscript. XJZ assisted in interpretation of data, and preparation of the manuscript. SSZ, YYP, CKZ and LW supervised the study. LW contributed to data collection. All authors have contributed to the interpretation and critically reviewed the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work. Funding This research was supported by the 2025 Annual Planning Project of Heilongjiang Province’s Education Science “14th Five-Year Plan” (ZJB1425057), the 2025 School-level Vocational Education Research Project of Heilongjiang Nursing College (20250203), the 2023 Teaching Reform Research Project of Dalian Medical University (DYLX23013), and 2021 Year Key Project of Education Science Planning of Heilongjiang Province (GJB1421313). Data availability The datasets during and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments, or comparable ethical standards. The study was approved by Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (approval number: KY2004-006) and all nursing undergraduates signed the informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Quigley BH, Renz S, Bradway C. Fall prevention and injury reduction utilizing virtual sitters in hospitalized patients: a literature review. CIN Comput Inform Nurs. 2021;39:929–34. [ DOI ] [ PubMed ] 2. 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[ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1 (141.4KB, pdf) Supplementary Material 2 (132.9KB, pdf) Data Availability Statement The datasets during and/or analyzed during the current study are available from the corresponding author on reasonable request. 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