Artificial Intelligence Literacy and Educational Impact among Iraqi Nursing Students: A Cross-Sectional Survey

Haider Mohammed Majeed; Ali Hussein Alek Al-Ganmi; Ali Dhahir Abdulyemmah; Tahseen Rajab Mohammed; Ahmed F. Hassan; Hassan Raysan Al‑Waeli; Abdulellah M Alsolais

doi:10.12688/f1000research.184799.1

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Research Article

Artificial Intelligence Literacy and Educational Impact among Iraqi Nursing Students: A Cross-Sectional Survey

[version 1; peer review: awaiting peer review]

Haider Mohammed Majeed ¹, Ali Hussein Alek Al-Ganmi¹, Ali Dhahir Abdulyemmah², [...] Tahseen Rajab Mohammed¹, Ahmed F. Hassan¹, Hassan Raysan Al‑Waeli³, Abdulellah M Alsolais⁴

Haider Mohammed Majeed ¹, Ali Hussein Alek Al-Ganmi¹, [...] Ali Dhahir Abdulyemmah², Tahseen Rajab Mohammed¹, Ahmed F. Hassan¹, Hassan Raysan Al‑Waeli³, Abdulellah M Alsolais⁴

PUBLISHED 16 Jul 2026

Author details Author details

¹ Adult Nursing, University of Baghdad -College of Nursing, Baghdad, Baghdad Governorate, 14149, Iraq
² Adult Nursing, Karbala Health Directorate, Al-Hindiya Teaching Hospital, Karbala, Karbala, 141407, Iraq
³ Adult Nursing, Health Directorate, Souq Al-Shuyoukh General Hospital, Thi-Qar, Iraq, Thi-Qar, Thi-Qar, 10235, Iraq
⁴ Adult Nursing, 1. Shaqra University , College of Applied Medical Sciences, Saudi Arabia, Saudi Arabia, 23532, Saudi Arabia

Haider Mohammed Majeed
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Ali Hussein Alek Al-Ganmi
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Writing – Original Draft Preparation, Writing – Review & Editing

Ali Dhahir Abdulyemmah
Roles: Conceptualization, Resources, Software, Validation, Writing – Original Draft Preparation

Tahseen Rajab Mohammed
Roles: Conceptualization, Data Curation, Funding Acquisition, Software, Supervision

Ahmed F. Hassan
Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Validation, Writing – Original Draft Preparation

Hassan Raysan Al‑Waeli
Roles: Conceptualization, Data Curation, Software, Supervision

Abdulellah M Alsolais
Roles: Funding Acquisition, Investigation, Methodology, Validation, Visualization

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

This article is included in the AI in Medicine and Healthcare collection.

Abstract

Background and Objectives

This study aimed to explore nursing students’ artificial intelligence literacy in terms of knowledge, attitudes, and practices and its impact on their educational input in Iraq.

Methods

A descriptive cross-sectional survey conducted across 100 nursing students from the College of Nursing, University of Baghdad, Iraq in 2025. The study utilized the Artificial Intelligence knowledge, attitudes, and practices scale. The Statistical Package for Social Sciences (SPSS), version 26 was used to examine the associations among the measured variables in the study.

Results

The study findings show that more than three quarter of nursing students (80%) possessed a high level of knowledge regarding artificial intelligence, whereas 12% demonstrated a moderate level and only 18% exhibited a low level of knowledge. Findings indicate a low prevalence of negative attitudes, with 59% of students demonstrating positive attitudes. Findings reveals that just over one-third (34%) of students reported practicing often based on the artificial intelligence. Correlational analyses revealed that nursing students age and the study academic year were significantly associated with their total score of artificial intelligence literacy in terms of knowledge, attitude, and practice (p < 0.001).

Conclusion

The study revealed that nursing students had a wavier level of artificial intelligence literacy of knowledge, attitude and practice and its application in nursing learning. Prioritize strategies should be applied in the nursing curricula to enhance students’ knowledge and translate positive attitudes into consistent practice, ultimately fostering effective integration of artificial intelligence in nursing education.

Keywords

Artificial Intelligence, literacy, knowledge, attitudes, practices nursing education, students, Iraq.

Corresponding author: Haider Mohammed Majeed

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2026 Mohammed Majeed H et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Mohammed Majeed H, Hussein Alek Al-Ganmi A, Dhahir Abdulyemmah A et al. Artificial Intelligence Literacy and Educational Impact among Iraqi Nursing Students: A Cross-Sectional Survey [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1169 (https://doi.org/10.12688/f1000research.184799.1) First published: 16 Jul 2026, 15:1169 (https://doi.org/10.12688/f1000research.184799.1) Latest published: 16 Jul 2026, 15:1169 (https://doi.org/10.12688/f1000research.184799.1)

Introduction

Artificial intelligence (AI) is a branch of computer science. It is an intelligent system that uses computer technology to simulate, extend, expand, and realize the human mind to carry out thinking activities, learn knowledge, and to help human beings solve problems.¹^,² Integrating artificial intelligence into healthcare has become a defining characteristic of the modern era, presenting both opportunities and challenges for the nursing profession. The AI technologies are widely regarded as transformative tools that enhance the quality and efficiency of healthcare delivery.³ They include clinical decision support systems, remote monitoring devices, and diagnostic tools, which offer vast amounts of clinically relevant data to improve patient outcomes and reduce medical errors.⁴ The AI has interference with the education and training of health professions’ students. To further illustrate, a computer-aided learning system has been constructed to help health professions’ students gain diagnostic experience by training this machine learning model using numerous clinical cases.⁵ Furthermore, resources for the implications of AI in medical training and education remain limited globally, particularly in the Middle East region.⁶ Recognizing the growing importance of AI in its incorporation in medical education for equipping future healthcare professionals with the skills and knowledge necessary to navigate the increasingly developing fields.⁷ As AI applications become more ubiquitous, understanding how university students perceive, engage with, and leverage AI is crucial for ensuring the relevance and effectiveness of educational programs. Wealthy nations have offered significant financial assistance for AI development, particularly in medicine. Specifically, generative AI is more widely used in nursing education while low-income countries need more substantial initiatives to use AI as there is a shortage of research on the subject. According to the World Health Organization, there will be a deficit of around 12.9 million healthcare professionals globally by 2035.⁸ Generative AI is a class of AI models including natural language processing, machine learning, reasoning and decision-making and other multi-disciplinary technologies, which can creatively generate images, text, phonetics, which represents a promising application in the field of nursing education.⁹ Currently, ChatGPT is one of the most powerful generative AI models, and its use not only meets the personalized learning needs of nursing students, but also improves the efficiency of educators and promotes collaboration and communication between teachers and students.¹⁰ ChatGPT simulates learning environments or hospital scenarios for nursing students through virtual reality, which is conducive to improving the students’ confidence and learning ability.¹¹ In addition, ChatGPT can provide nursing students with timely learning feedback, meet the need for rapid access to information, and improve time management skills.¹²^,¹³ As AI continues to reshape the landscape of healthcare and medical education, it is essential to understand how future healthcare professionals interact with and adapt to these technologies. This study is particularly significant in the context of the Middle East, where the integration of AI into nursing education is still emerging and under-researched. Therefore, this study aimed to explore nursing students’ knowledge, attitudes, and practices regarding the use of artificial intelligence, particularly generative AI tools such as ChatGPT, in their education. This study seeks to assess the level of awareness among nursing students, their willingness to engage with AI technologies, and how these tools influence their learning processes, confidence, and academic performance.

Materials and methods

Study design and setting

A descriptive cross-sectional survey conducted among 100 convenient sample of nursing students from the College of Nursing, University of Baghdad between June and November, 2025. This college supplied several scientifically and practically qualified graduates’ nurses with scientific degrees, including bachelor’s, higher diploma, master’s, and doctoral in a variety of nursing specializations, to health and educational institutions. This college is also providing the resources needed for research activity and instructional programs.

Sample and sampling

The study was carried out among undergraduate nursing students pursuing the Bachelor of Science in Nursing at College of Nursing, University of Baghdad, with the involvement of undergraduate nursing students from third-year to final-year levels to ensure a broad representation of educational stages. Inclusion criteria were: (1) enrollment in the third or fourth year, age group between 20–25 years, and (3) consent to participate. First- and second-year students were excluded because they had less exposure to advanced nursing coursework and fewer opportunities to engage in clinical learning supported by digital tools. A convenience sampling approach was used due to feasibility within a single institutional setting and the exploratory nature of the study. Out of the total respondents, 100 valid responses were included in the analysis. Fifteen participants were excluded due to in response to full google forum questionnaire based on deadline. The process of recruitment was organized with the help of the faculty coordinators and class representatives, who sent all eligible students the survey link using official academic communication tools, such as Google class room, institutional formal email, and academic forums. The main limitations of convenience sampling (selection bias and limited generalizability) are acknowledged in the section.

Data collection and study instruments

Data collection was done through the structured and standardized questionnaire, which was based on the already published and verified studies of knowledge, attitudes, and practice toward artificial intelligence in Nursing education (Mariano et al., 2025).¹⁴ For cultural aspects, however, some contextual modifications were made to fit the local academic context without tampering with the original validity of the content.

Content validity was assessed by a panel of three experts in nursing education and educational research who evaluated relevance, clarity, and alignment with study objectives. Revisions were made based on their feedback. A pilot test was conducted with 10 eligible nursing students who were not included in the final sample. Pilot feedback confirmed feasibility and clarity, and minor wording refinements were made. The final questionnaire consisted of two sections: Section 1: Sociodemographic and academic characteristics age group, sex, marital status, academic year, and residence were recorded. Section 2: AI literacy domains consists students’ knowledge (10 “multiple-choice” (MCQ) questions), attitudes (12 questions), and practices (12 questions). The knowledge section utilized a scoring system where each multiple-choice question was assigned a correct (1) or incorrect (0) score, and the total score was calculated accordingly. For the attitude and practice scales, a 5-point Likert scale with responses recorded as (“strongly disagree,” “disagree,” “don’t know,” “agree,” “strongly agree”) to assess their attitude and practice towards AI. The scoring system was based on the mean score of each item and the mean of the total scores of each scale. Internal consistency was evaluated using Cronbach’s alpha. Reliability coefficients were tested by previous authors (Mariano et al., 2025)¹⁴ and deems acceptable for all subscales: knowledge (α = 0.91), attitudes (α = 0.88), and practices (α = 0.79). The study was conducted using an online survey from June to November 2025. The survey was prepared and sent to the nursing students in the University of Baghdad using online platforms such as (Google Forms, e-mail; Google Classroom) and academic forums, where applicable. Participants were notified that all questions in the survey should be answered before submission to avoid data missing. An informed written consent forms was sent electronically for all participants prior survey to explain that participation is voluntary. All completed questionnaires were collected then processed it to assess participants’ knowledge, attitudes, and practices toward AI.

Ethical considerations

Official permissions were obtained from relevant authorities before collecting the study data as started by getting the approval of the Council of the Nursing College/University of Baghdad for this study on (Issue No. TGS18/12/2024). This in concordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data were collected anonymously, and no names or identity information were required from the participants.¹⁴^,¹⁵ The current study strictly adhered to all ethical considerations of scientific research. A written consent was obtained from each participant before enrollment in the study by selecting acceptance to participate in the survey. This acceptance was considered informed written consent for enrollment in the current study. Throughout the survey, confidentiality was maintained as no questions were asked regarding participants’ identities or personal information. Participants were informed that the study would be used solely for scientific purposes.

Statistical analysis

Data were retrieved, cleaned, and analyzed using the Statistical Package for Social Sciences software (SPSS) version 24. Descriptive analyses, such as frequency and percentage¹⁶^,¹⁷ or mean and standard deviation, were employed to analyze participants’ demographic variables, knowledge, attitude, and practice regarding AI. The association between Participants’ knowledge, attitude, and practice regarding AI was Estimated using Spearman’s correlation coefficient test. A p-value of ≤0.05 was considered statistically significant.

Results

The study results revealed that half of students (50%) were within the age group of 22–23 years, with male gender constituted the most of the sample (65%). The majority of students were single (77%), and the sample was almost equally distributed between the third stage (57%) and fourth stage (43%), providing balanced representation. Also, the majority of students lived with their families (72%). These distributions are consistent with typical nursing student populations, characterized by young age, predominance of females, and high proportion of single status ( Table 1).

Table 1. Socio-demographic characteristics of the study participants.

Demographic variables	Variables	Frequency	Percentage
Age group	20-21 Year	28	28%
	22–23 Year	50	50%
	24-25 Year	22	22%
Sex	Male	65	65%
Sex	Female	35	35%
Marital status	Single	77	77%
Marital status	Married	23	23%
Academic Year	Third Stage	57	57%
Academic Year	Four Stage	43	43%
Residency	With Family	72	72%
Residency	Campus	28	28%

The Pearson correlation analysis indicate a strong positive with high significant relationship between knowledge and practice (r = 0.138, p = 0.01). This suggests that a higher level of knowledge does not necessarily translate into improved practice. However, a statistically significant positive correlation was observed between attitude and practice (r = 0.169, p = 0.012), indicating that positive attitudes are more likely to influence and improve practical behaviors. In contrast, the correlation between knowledge and attitude was negligible and statistically non-significant (r = 0.148, p = 0.832) ( Table 2). These findings imply that knowledge and attitudes play a stronger role in shaping practice compared to attitude alone.

Table 2. Pearson correlation coefficients between knowledge, attitude, and practice score.

Variable	Pearson correlation	P. value
Knowledge	1	0.01**
Practice	0.138	0.01**
Attitude	1	0.012**
Practice	0.169	0.012**
Knowledge	1	0.832
Attitude	.148	0.832

The study results also reveal that more than three quarter of the participants (80%) have a good knowledge about using AI, while 18% demonstrate fair knowledge, and only 18% have poor knowledge. With respect to attitudes, 59% of students express positive views about AI, while 27% remained neutral. Negative attitudes were uncommon, as only 14% disagreed or strongly disagreed.

Regarding practice, (34%) of students reported engaging often in the practice of AI, follow by (23%) of them engage rarely, and (13%) of them most of the time, while 18% of student never practicing on AI, only 12% of them report practicing AI all the time ( Table 3). These results demonstrate that although knowledge levels were relatively high and attitudes were predominantly positive, the translation of knowledge into consistent practice remains limited.

Table 3. The overall level of knowledge, attitude, and practice of nursing student on artificial intelligence.

Knowledge level	Frequency	Percentage
Poor	18	18.0
Fair	12	12.0
Good	80	80.0

Attitudes level	Frequency	Percent
Strongly Disagree	8	8.0
Disagree	6	6.0
Neutral	27	27.0
Agree	41	41.0
Strongly Agree	18	18.0

Practice level	Frequency	Percent
Never	18	18.0
Rarely	23	23.0
Often	34	37.0
Most of the time	13	13.0
All the time	12	12.0

The study results show a strong and highly significant correlation between students age (r = 0.816; p = 0.000), academic year (r = 0.219; p = 0.015) and their knowledge about AI, respectively. Students’ marital status also highlights a moderate significant relationship (r = 0.219; p = 0.010), while their sex was weakly associated with knowledge (r = 0.267; p = 0.047). Residency did not show a significant correlation. For attitude, students age (r = 0.655; p = 0.000), their academic year (r = 0.167; p = 0.09) were significant factors, while sex, marital status, and residence were not. Similarly, for practice, students age (r = 0.698; p = 0.000) and their academic year (r = 0.198; p = 0.009) were significantly correlated, while the other socio-demographic factors did not demonstrate meaningful associations ( Table 4). Collectively, these findings highlight that both students age and academic year are the most influential factors affecting their AI knowledge, attitudes, and practices.

Table 4. Correlation between nursing students socio-demographic characteristics and their knowledge, attitude, and practice regarding AI.

Level of knowledge	Correlation coefficient	P value
Age	0.816^**	0.000*
Marital status	0.311^**	.010**
Academic Year	0.219^**	.0015**
Residence	0.281	0.076
Sex	0.267^*	0.047*

Level of attitudes	Correlation coefficient	P value
Age	0.655	0.000**
Marital status	0.312	0.036*
Academic Year	0.167	0.009**
Residence	0.371	0.076
Sex	0.239	0.436

Level of practices	Correlation coefficient	P value
Age	0.698	0.000**
Marital status	0.236	0.332
Academic Year	0.198	0.009**
Residence	0.236	0.089
Sex	0.156	0.369

* P-value ≤ 0.001.

Investigated the association between students KAP with their socio demographics information shows a significant association between their ages with AI practice (p = 0.026), attitude (p = 0.004), and knowledge (p = 0.000). Students sex shows a statistically significant association with their knowledge about AI only (p = 0.033), but not with their practice or attitude. Students’ marital status, academic year, and type of residency are not significantly associated with any of the three outcome variables ( Table 5). These results reinforce the conclusion that age is the most important demographic predictor across all domains, while sex exerts a minor effect limited to knowledge.

Table 5. Association between socio-demographic characteristics of nursing students with their Knowledge, attitude and practice related to AI.

Variables	Practice		Attitude		knowledge
	Value	P value	Value	P value	Value	P value
Age	1.865	0.026	1.497	0.004**	0.756	0.000**
Sex	0.544	0.361	0.366	0.138	0.268	0.033*
Marital status	0.349	0.199	0.398	0.469	0.296	0.989
Academic Year	0.471	0.219	0.863	0.245	0.280	0.439
Residence	0.431	0.169	0.542	0.639	0.357	0.076

* P-value ≤ 0.001.

Discussion

Artificial intelligence (AI) is developing rapidly and has become an integral part of daily life.¹⁸ This technological advancement has transformed both education and practice. AI, widely accepted in computer sciences, changes the way individuals search for information, communicate, and organize daily activities.¹⁹ Since nursing students play a critical role in teaching, clinical practice, and care delivery, they must acquire the knowledge, skills, and competencies required to work effectively with AI.²⁰ The findings of the current study demonstrates that the majority of nursing students were male, and these findings is in line of previous studies regarding AI.²¹^–²⁵ Additionally, the current study reveals that the majority of students were single and this is similar to various studies indicated that most of study participants unmarried.²⁶^–³⁰ Moreover, the current study reveals high percentage of student were in the fourth academic year than third year which, imply that this distributed help to get more accurate results about IA and prevent bias in current study these results are accordance with numerous study who reports that. This result is a mirror of a Palestinian study results showed that the majority the students were in the fourth year.³¹

The findings of the present study demonstrate a generally high level of knowledge, a moderate-to-high level of practice, and a predominantly positive attitude toward AI among nursing students. The current study found that more than half of nursing students exhibited good knowledge regarding AI. This finding aligns with a recent study conducted in Saudi Arabia revealed that approximately three-fourths of nursing students reported understanding the basic computational principles of AI and possessing a strong overall knowledge and awareness of the technology.³² The results indicate that participants had varying and satisfactory levels of knowledge about AI and its applications in healthcare. While the correct response rates were generally higher than the incorrect, there were still some questions that many participants answered incorrectly.³³

In parallel, this study showed a significant association between students’ sex and their AI knowledge scores (p = 0.045). This finding is consistent with study result demonstrated there are significant relationship between sex and knowledge regarding AI.³⁴ This suggests that male and female participants differed in knowledge levels, possibly due to variations in educational exposure, access to information, or personal interest. Correlation analysis indicated that students age was positively associated with high knowledge about AI. This result aligns with previous studies reveals significant associations between students’ genders and the level of knowledge about AI.³⁴^–³⁵ These results imply that older students, those in higher academic years might owned crowded knowledge due to their experiences with various tasks from previous study years. The participants may acquire more knowledge through cumulative academic and life experiences.

For attitudes, there was a strong and highly significant relationship with students age, indicating that maturity may influence perceptions toward AI. Other demographic variables such as sex, marital status, academic year, and residence did not show significant associations. This aligns with previous research indicating that attitudinal changes are more affected by maturity than by other demographic factors.²⁶

In terms of practicing AI, results showed a significant association with students age, while other socio-demographics were not significantly related. Correlation analysis confirmed that age and academic year of students were significantly related to their AI practice scores, suggesting that older and more academically advanced students may more effectively apply their AI knowledge and attitudes in practice. A study conducted in Sudan similarly reported that age and sex significantly affected students AI practices (p < 0.05).³⁴

In the current study of Iraqi nursing students, AI knowledge and practice were significantly correlated, as were practice and attitudes, whereas knowledge and attitudes showed no significant association. These findings partially align with previous research reported that nurses were positively correlated with both attitude (r = 0.311, p < 0.001) and application of AI (r = 0.514, p < 0.001).³⁶ Likewise, Mariano et al. found that Saudi nurses, faculty, and students had satisfactory AI knowledge and mildly positive attitudes, with all KAP components significantly interrelated.³³ These studies support the current study, which observed a knowledge-practice link. In Jordan, Oweidat et al. reported that higher AI attitude and practice scores strongly predicted nurses’ intent to stay (attitude: r = 0.64, β = 0.34, p < 0.001; practice: r = 0.58, β = 0.29, p < 0.001).³⁵ In contrast, Rony et al. found a very strong knowledge-attitude correlation (r = 0.89, p < 0.001), which differs from the current study results, possibly due to differences in curricula or exposure.³⁶

Several studies emphasize that AI knowledge remains modest even as attitudes are generally favorable. Namdar et al. reported that 41.1% of Iranian nurses had low AI knowledge,²⁶ and Wang et al. found that most Chinese healthcare participants knew “only a little” about AI in nursing (57% limited knowledge) despite positive attitudes.³⁶ Similarly, Sommer et al. in Germany observed that only 25.2% of nurses considered themselves AI experts, yet 65.7% viewed AI as an opportunity.³⁷ Lukić et al. reported that Croatian first-year nursing students had significantly positive AI attitude scores (mean 64.5/100, p < 0.001 vs. neutral).³⁸ In Palestine, Salama et al. found that 79% of nursing students supported AI integration into curricula, although 69.9% had no formal AI training.³⁹ These findings are similar to the results of the current study show that optimism often coexists with knowledge gaps.

Importantly, AI experience and training appear to enhance KAP. This study shed light to inform educators, curriculum designers, and policymakers about the potential benefits and challenges of adopting AI-driven tools in nursing programs. The findings may contribute to the development of more effective, technology-enhanced learning environments that can help address the global shortage of healthcare professionals and improve the overall quality of nursing education.

Study strengths and limitations

To the best of our knowledge, this is the first study to investigate nursing students’ artificial intelligence literacy in terms of knowledge, attitudes, and practices and its impact on their educational input in Iraq. This study utilized social media to collect data, which enables low-cost access to sizable and dispersed groups of nursing students. Students’ familiarity with social media platforms can also boost engagement and participation. On the other hand, only students who are active on social media will be contacted, which can underrepresent students who are not as tech-savvy. Additionally, compared to face-to-face techniques, online surveys typically have greater non-completion rates, and it can be challenging to verify that respondents are actually nursing students without institutional identification. A number of approaches can be suggested for further study in order to overcome these limitations. Requesting a university-issued email address or student identification number from responders helps improve participant status verification while preserving anonymity throughout the study.

Conclusion

The study revealed that nursing students had wavier level of artificial intelligence literacy of knowledge, attitude and practice and its application in nursing learning. These findings suggest that nursing students’ positive attitudes play a more influential role in shaping practical engagement with artificial intelligence than knowledge alone. Prioritize strategies should be applied in the nursing curricula to enhance students’ knowledge and translate positive attitudes into consistent practice, ultimately fostering effective integration of artificial intelligence in nursing education.

Data availability

The dataset for this study is openly available on Zenodo at https://doi.org/10.5281/zenodo.21160212. Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver [CC0 1.0 Public domain dedication].⁴⁰

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