Preparedness and Attitudes of Nursing Students toward the Integration of Artificial Intelligence in Nursing Education

1.1. Research Aims

The present study primarily aims to explore student nurses’ readiness and willingness to integrate AI into their nursing program. Alternatively, even to investigate the possible advantages, barriers, and training deficit of AI implementation in nursing education.

1.2. Research Questions

1. How much do nursing students feel that they are ready for AI integration in their education?
2. What is the perception of nursing students in relation to AI integration into their learning experience?
3. What are the students' perceived advantages and challenges of AI implementation in nursing education?
4. What supports or training do nurse students need for effective use of AI tools?

2.1. Research Design

This study adopted a descriptive cross-sectional survey design to assess nursing students’ preparedness and attitudes toward AI integration in their education. This design collects data from nursing students at one specific time, aiming to describe participants’ characteristics, preparedness, and attitudes toward integrating AI in nursing education. It also gathers data about the perceived benefits and barriers of the use of AI in nursing education.

2.2. Participants and Setting

The population of this study comprises undergraduate nursing students from the bachelor's degree program at Jerash University. All study levels (1st to 4th year) are encouraged to get involved. A survey method was adopted to select the respondents by convenience sampling. Students were recruited for the survey by posting an invitation in their WhatsApp groups and in classrooms. A sample of 180 students from Jerash University was targeted for the study. The sample size was determined using the G Power statistical software program (version 3.1.9.4), and an ANOVA test was run with α=0.05 and a power size of 0.8. A total of 400 nursing students were assessed for eligibility during the study period. Among the eligible students, 192 agreed to participate and completed the questionnaire, yielding a response rate of 48%. Questionnaires with substantial missing data or incomplete responses (n = 76) were excluded from the analysis. Consequently, data from [122] participants were included in the final statistical analysis.

2.3. Instrument

The primary outcomes of this study were nursing students' preparedness for integrating artificial intelligence (AI) into nursing education and their attitudes toward AI integration.

A three-section self-administered questionnaire developed by the research team was used for data gathering. The first section is about the demographic data of the participants. It consists of 7 items to collect data bout age, gender, education level, previous training on AI, participants’ use of AI, frequency of AI use, and AI tools used. The second section gathers data about students’ preparedness to use AI in nursing education. It consists of 6 Likert scale items with responses ranging from strongly disagree (1) to strongly agree (5). The third section gathers data about students’ attitudes toward integrating AI in nursing education. It consists of 9 Likert scale items with responses ranging from strongly disagree (1) to strongly agree (5). The fourth section gathers data on the benefits, barriers, and suggestions for integrating AI into nursing education through open-ended questions.

2.4. Data Collection

During data collection, the researchers distributed the questionnaire directly to students in the classroom. This approach typically began with the researchers providing a brief explanation of the study’s purpose and instructions for completing the questionnaire, ensuring that students understood the confidentiality and voluntary nature of their participation. The questionnaires were then handed out to all students present, allowing them to complete the forms individually and in a controlled environment, which helps minimize external influences and ensures a higher response rate. The researchers remained available to clarify any questions or ambiguities regarding the items, thereby supporting accurate and reliable responses. Once completed, the questionnaires were collected immediately by the researchers, reducing the risk of loss or incomplete data and facilitating efficient data management for subsequent analysis. Data were collected between July and August 2025.

Several measures were implemented to minimize potential sources of bias. Selection bias was reduced by inviting all eligible nursing students within the target population to participate and by applying uniform inclusion criteria. To reduce information bias, data were collected using a standardized, structured questionnaire based on previously validated instruments and relevant literature. Prior to data collection, the questionnaire was pilot-tested to ensure clarity, comprehensibility, and content validity.

Response bias and social desirability bias were minimized by ensuring participant anonymity and confidentiality and by informing participants that their responses would be used solely for research purposes and would not affect their academic standing. Participation was entirely voluntary, and students could withdraw at any time without penalty.

2.5. Data Analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS, version 25) program. Descriptive statistics in terms of the mean and standard deviation were used to describe the demographic data when they were continuous and normally distributed. The frequencies and percentages were used when the demographic data were categorical. Inferential statistics (Independent samples t-test, NOVA, and Correlation) were used to test the relationship between the demographic data and students' preparedness and attitude toward the integration of AI in nursing education. Qualitative responses (Section four) were analyzed thematically to identify common themes.

3.1. Characteristics of the Study Sample

The characteristics of the study sample may help to obtain more accurate data and play a crucial role in explaining the study results. Table 1 shows the demographic characteristics of the study sample. Regarding age, the results showed that the students' ages ranged from 18 to 38 years, with a mean age of 23.73 years. For gender, the results illustrated that 64 (52.5%) of the participants were female. For previous training on AI, the majority of the participants (n=90, 73.8%) did not receive training on the use of AI applications. The results showed that the vast majority of the students (n=110, 90.2%) use AI applications, and the ” sometimes” response was the most frequent answer (n=42, 34.4%). However, ChatGPT was the most commonly used application by the participants (n=80, 65.6%). For the students' level of education, a large number of the participants were fourth-year students (n=76, 62.3%).

3.2. Nurses' Preparedness toward AI Integration in Nursing Education

To evaluate nurses' preparedness toward AI usage in nursing education, six items were assessed on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The results are summarized in Table 2. The overall preparedness toward AI among nursing students was moderate, with a mean score of 3.36 (SD = 1.15). The highest-rated item was confidence in learning and using AI tools in nursing education (M = 3.60, SD = 1.03). Awareness of ethical and privacy concerns also scored relatively high (M = 3.47, SD = 1.16). The lowest-rated item was program preparation for AI in healthcare (M = 3.11, SD = 1.33). Students’ knowledge of AI applications in clinical practice was also relatively low (M = 3.18, SD = 1.21). Access to AI-related resources (M = 3.34, SD = 1.19) was moderate.

3.3. Nurses' Attitudes toward AI Integration in Nursing Education

This section presents findings on nurses’ attitudes toward the integration of Artificial Intelligence (AI) into nursing education. Nine items were evaluated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Results are presented in Table 3.

The overall attitudes toward AI were generally positive, with a mean score for all items of (3.75). The highest-rated items were: 1. AI helps students better understand complex topics (M = 3.98, SD = 0.95). 2. Using AI can improve the quality of students’ academic work (M = 3.97, SD = 0.93). 3. AI can support students in organizing and structuring their assignments (M = 3.91, SD = 0.95). The lowest-rated item was excitement about using AI in nursing education (M = 3.46, SD = 1.06). Moderate agreement was found regarding AI improving nursing education quality (M = 3.64, SD = 1.10) and providing useful feedback to improve assignments (M = 3.70, SD = 0.97).

3.4. Difference in Preparedness or Attitudes based on Demographic Factors

This section explores whether demographic characteristics (age, gender, training, AI usage, frequency of use, applications used, and academic level) influenced nursing students’ preparedness and attitudes toward AI integration. The results are presented in Table 4.

3.5. Perceived Benefits, Challenges, and Training Needs of AI in Nursing Education

This section explores students’ perceptions of the benefits, challenges, and training needs regarding Artificial Intelligence (AI) in nursing education. The findings are summarized in Table 5.

4.1. Demographic Characteristics

The demographics of the sample in this study are consistent with the AI use demographics at universities across the world. The majority of the participants were young adults, predominantly women, and aged 21 to 23. The pattern of people widely using AI applications without established conventions reflects research that found learners frequently use ChatGPT for their homework, even when they do not have institutional guidance to support them [24-26]. Alignment of ChatGPT as the leading AI tool is concordant with global surveys that have identified it as the most prominent and popular generative AI application amongst students for brainstorming ideas and assignments [24-27]. The fact that fourth-year students prevail in the sample could suggest higher academic pressure on them, or prior experience with digital means and machines might imply they are more likely to actively use AI for learning purposes, which agrees with previous literature. However, the limited exposure to formal AI training reflects a digital literacy gap and demands that intervention mechanisms be developed to promote the ethical use of AI technologies within higher education and effectively [25, 27, 28].

4.2. Nurses' Readiness for AI in Nursing Education

The findings indicate that nursing students show a moderate level of readiness to integrate AI into the education process (M = 3.36). The findings suggest that while nursing students are not entirely unprepared, there are gaps in preparing the nursing workforce to deal with AI in healthcare.

The item with the highest score (students' confidence in using and learning AI tools, M = 3.60) is a good indicator of their attitudes. This discovery shows digital mechanics who are primarily open to technological advances. This confidence is a sturdy foundation upon which to build more systematic curricula aimed at developing AI literacy. As Robert N. [29] has also observed, the uptake of AI will be influenced by both the technology and end-user readiness to interact with it. Schools must use this existing confidence to drive the usage of AI-based learning modules and tools.

Besides the low knowledge score, participants also reported a moderate-to-high awareness towards ethical and privacy issues (M = 3.47), while this attitude is in line with the caring nature of nursing as a profession. The impact of AI on patient data privacy and the dehumanization of care [28] should be highlighted to students. However, as knowledge alone is not enough, formal education on AI ethical frameworks is essential if current practice is to change in the future.

On the contrary, rated on the lowest end of the scale is perception about program preparedness for AI in health care (M = 3.11), signifying a deflection in what nursing education should be currently practicing. This finding is supported by contemporary literature showing that there is a delay between the rapid development of AI in clinical settings and integration into nursing curricula [30]. Students seem to believe that their programs are not training them appropriately for the technological transition. This lack, if it can be called that, is further supported by the low mean for the medical AI application (M = 3.18). If nursing students do not know how to work with AI-coding in diagnostic prediction methodologies or on patient surveillance, they cannot make sense of it for their learning process and develop the critical thinking capacity to understand recommendations based on artificial intelligence. The mean access to AI-related resources (M = 3.34) falls into an accessible range. Without exposure to software, simulation tools, or curated learning content, even the most self-assured students may become preoccupied with becoming proficient in their use. However, the results of this study highlight the need for AI infrastructure and resources to train theoretical experts into practical expertise.

4.3. Students' Attitude toward AI Integration

Overall, a positive attitude toward AI (M = 3.75) may indicate that nursing students recognize the value of using AI academically to better understand complex concepts and achieve academic success. This aligns with reports from other studies showing that nurses have positive attitudes toward AI as a beneficial tool for improving both clinical decision-making and education. However, the mean score of “excitement” is the lowest (M = 3.46), revealing a critical nuance that they are aware that AI is useful but may not be excited about it due to moral issues and fear of loss of humanization. This circumspect optimism means that acceptance is also provisional, or contingent. The discrepancy between the high utility scores and lower excitement indicates that hands-on interactive AI training that is not only instructional, but confidence-building could be required. Furthermore, demographic correlates should be studied in the context of prior research demonstrating that previous exposure and frequency are significant predictors of both preparedness and attitudes. Therefore, education interventions suited to attenuate local demographic disparities should be required rather than a one-size-fits-all approach, in which we consider a workforce prepared and enthusiastic about AI to transform health care.

4.4. Difference in Preparedness or Attitudes based on Demographic Factors

Results of this study indicate that demographic variables such as age and gender are weak predictors of AI readiness compared to experience-related variables. Educational and private engagement with AI are the strongest predictors of AI literacy, underscoring that explicit learning and exposure play a fundamental role in shaping skills and attitudes. Additional consideration is given to the school year and its strong relationship with attitudes, suggesting a potential benefit of being exposed for a longer time in the clinic to more advanced aspects of this technology.

Pragmatic students recognize that AI can enhance efficiency and learning, and they do not concentrate on the nature of technology as much as the reliability or what all this means for academic integrity. Concern about misinformation and plagiarism rates reflects the immediate need to develop critical thinking as well as skill training. Notably, students appear to seek not only access to AI tools, which are already freely available, but also comprehensive training on their effective use, ethical implications, and the critical evaluation of AI-generated outputs. This clear ask for help surfaces a large void that schools of nursing must address if there is to be responsible and effective movement of AI into practice [31].

4.5. Implications for Nursing Education

Nursing programs need to take a proactive and multilevel approach. The researchers recommend that curricula be revised to include formal education on the basics of AI and its common use cases in health care, as well as basic data literacy. Second, the curriculum of the AI ethics case study needs to be distributed across the curriculum, grounding as much as possible ethical awareness in practice. Third, collaborations with medical facilities using AI could provide students with practical experience and access to actual resources. Last, but not least, faculty development is a key component to prepare educators to teach and mentor students in this emerging field [31].

4.6. Limitations of the Study

The researchers acknowledge the limitations of our study's reliance on self-reported data from a single institution, with potential for restricted generalizability. Subsequent studies will also need to explore the precise types of AI resources students perceive as most helpful, and explore any association between being prepared for AI in relation to clinical reasoning skills. Furthermore, studies that follow students through their training to evaluate the impact of changing curricula on student preparedness over time are needed.