# data[].attributes.body
SABZALIEVA, E. and VALENTINI, A., 2023. ChatGPT and artificial intelligence in higher education: quick start guide. Paris, FR: United Nations Educational, Scientific and Cultural Organization.

# data[].attributes.body
SHARMA, M. and SHARMA, S., 2023. A holistic approach to remote patient monitoring, fueled by ChatGPT and Metaverse technology: the future of nursing education. Nurse Education Today, 131, pp. 1-4. Available from: https://doi.org/10.1016/j.nedt.2023.105972 [Accessed 26 November 2023].

# data[].attributes.body
This is a brief, useful instructional guide for educators on using generative artificial intelligence in higher education. It was produced by UNESCO and the International Institute for Higher Education in Latin America and the Caribbean, with whom its authors are both affiliated. This guide uses clear language and helpful graphics to explain how to register for a ChatGPT account, prompting in ChatGPT, applications of ChatGPT in higher education, and the challenges and ethical implications of these tools.

The first author, Emma Sabzalieva, PhD, is a professor of higher education leadership and policy at McGill University in Canada and has an h-index of 7. The citation impact of this guide is 46. UNESCO is a renowned branch of the United Nations focused on peacebuilding through international teamwork in arts, culture, education, and science.

# data[].attributes.attachment_link.description
Download this HD photo of patient, photography, women and eternity by Getty Images (@gettyimages)

# data[].attributes.body
ANA CENTER FOR ETHICS AND HUMAN RIGHTS, 2022. Position statement: the ethical use of artificial intelligence in nursing practice. [online]. Silver Spring, MD: American Nurses Association. Available from: https://www.nursingworld.org/~48f653/globalassets/practiceandpolicy/nursing-excellence/ana-position-statements/the-ethical-use-of-artificial-intelligence-in-nursing-practice_bod-approved-12_20_22.pdf [Accessed 28 November 2023].

# data[].attributes.body
This position statement from the American Nurses Association (ANA) incorporates a brief literature review that informs best practices regarding the use of artificial intelligence (AI) in nursing in accordance with the profession’s code of ethics. The ANA is the profession’s preeminent U.S. organization and represents more than 4 million registered nurses. The ANA publishes the Code of Ethics for Nurses with Interpretive Statements and works to improve healthcare for all.

This statement makes several recommendations:

The use of AI in nursing practice should not replace nurses’ accountability, knowledge or skill.

Nurses should be aware of the potential for systemic racism and bias in AI programming.

Nurses must protect patients’ personal health data.

Nurses should integrate AI tools into practice when appropriate to support patient care.

There is no stated methodology, acknowledgment of limitations, or questions for further investigation included in this statement.

# data[].attributes.body
BROWN, J. et al., 2020. Student nurses’ digital literacy levels: lessons for curricula. CIN: Computers, Informatics, Nursing, 38(9), pp. 451-458.

# data[].attributes.body
This peer-reviewed article describes an observational study using descriptive, cross-sectional design in which a self-report survey was administered using purposive sampling to pre-registration masters nursing students to explore their perceived digital literacy skills. The authors are affiliated with two Australian universities: Curtin University and the University of Melbourne. The first author, Janie Brown, PhD, has an h-index of 16. The article’s citation impact is 16. The publishing journal’s five-year journal impact factor is 2.0. Authors’ recommendations related to student nurses’ digital literacy levels include:

embedding nursing informatics and digital literacy competencies in nursing curricula,

peer-/mentor-led teaching, and

replication of technologies used in nursing practice within clinical laboratories and simulation.

Limitations of the study are a small sample size (96 participants) and limited generalizability of results. The authors recommend further study aimed at surveying a more heterogeneous sample of nursing students using the survey, Self-Assessment Nursing Informatics Competencies Scale - SF30 (SANICS).

# data[].attributes.body
KING’S COLLEGE LONDON, 2023. Generative AI in higher education. [online]. London, UK: King’s College London. Available from: https://www.kcl.ac.uk/short-courses/generative-ai-in-he [Accessed 28 November 2023].

# data[].attributes.body
This is a free, online short course developed by King’s College London, featuring three lecturers, Dr. Martin Compton, Dr. Charlotte Haberstroh, and Dr. Oguz A. Acar, experts in curriculum design, education, and business innovation. The course is designed for those with interest in higher education, including educators. It is self-paced and can be completed in two weeks. The content of the course includes a basic overview of generative AI, its limitations and capabilities related to teaching, learning, assessment and evaluation, ethical and sustainability implications, and the impact of AI on society and employability. King’s College London is an internationally renowned university with a strong research focus.

# data[].attributes.body
LABRAGUE, L.J. et al., 2023. Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: a cross-sectional study. Nurse Education Today, 130, pp. 1-7. Available from: https://doi.org/10.1016/j.nedt.2023.105945 [Accessed 26 November 2023].

# data[].attributes.body
This peer-reviewed article details a cross-sectional study in which structured questionnaires were given to 323 nursing students in a BSN program at a public university in the Philippines. The study explores factors impacting students’ perception of readiness and barriers to using AI technology, including technological proficiency, understanding of AI, computer skills, and time constraints. The authors are affiliated with two universities, Loyola University and Samar State University. The first author, Leodoro J. Labrague has an h-index of 36. The article’s citation impact is zero. Altmetrics indicate 37 readers of the article. Nurse Education Today’s five-year journal impact factor is 4.5.

Authors’ recommendations for nursing faculty include providing opportunities for hands-on practice using AI technologies and support developing understanding of AI and technological proficiency. Limitations of the study are response bias related to self-reported data and limited generalizability. Further study with a more diverse sample of nursing students is recommended.

# data[].attributes.body
LIU, J. et al., 2023. The application of chat generative pre-trained transformer in nursing education. Nursing Outlook, 71, pp. 1-5. Available from: https://doi.org/10.1016/j.outlook.2023.102064 [Accessed 26 November 2023].

# data[].attributes.body
This peer-reviewed comprehensive literature review explores the benefits and challenges of the application of ChatGPT in nursing education. Benefits include personalized learning, creation of simulation scenarios, accelerated feedback/evaluation, and reduction of educator workload. Challenges include limited accuracy of responses, reduced human interaction, maintenance of privacy/security, potential bias, disparities in access, potential of reduced critical thinking skills, and plagiarism. Authors recommend a balanced approach to integrating ChatGPT into nursing education characterized by open-mindedness to advanced technology and preservation of the inherently human element of nursing.

The authors are affiliated with departments of medical informatics, otolaryngology, and nursing at Sichuan University and Vanderbilt University Medical Center. Three of the four authors are well-published and cited, with h-indices of 10 or 11. As this article was published in November 2023, it has a current citation impact of zero. Nursing Outlook has a five-year journal impact factor of 3.9.

# data[].attributes.body
LOKMIC-TOMKINS, Z. et al., 2021. An interdisciplinary partnership approach to improving the digital literacy skills of nursing students to become digitally fluent, work-ready graduates. In: M. HONEY et al., eds. Studies in Health Technology and Informatics: Nurses and Midwives in the Digital Age - Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics. 23 August-2 September 2021. Amsterdam: IOS Press. pp. 103-107.

# data[].attributes.body
This is a conference paper from the 15th International Congress in Nursing Informatics held in 2021. This paper outlines a collaboration between nursing faculty, university library services, and an academic skills service to develop and embed a digital literacy module for nurses into a graduate entry to practice nursing program at the University of Melbourne in Australia. Development of this digital literacy module was deemed necessary to address low digital literacy levels among student nurses and equip them with the skills needed to succeed in their nursing studies and to deliver optimal patient care in the digitally driven healthcare work environment.

All authors of this paper are affiliated with the University of Melbourne in either the Department of Nursing, library services, or the academic skills center. The first author, Zerina Lokmic-Tomkins, has an h-index of 17. The citation impact of this paper is 1. Altmetrics indicate 38 readers of this paper.

# data[].attributes.body
O’CONNOR, S. et al., 2023. Artificial intelligence in nursing education 1: strengths and weaknesses. Nursing Times, 119(10), pp. 1-4. Available from: https://www.nursingtimes.net/roles/nurse-educators/artificial-intelligence-in-nursing-education-1-strengths-and-weaknesses-11-09-2023/ [Accessed 26 November 2023].

# data[].attributes.body
This double-blind peer-reviewed magazine article is a clinical practice discussion regarding the strengths and weaknesses of artificial intelligence in nursing education. The article outlines many ways that generative AI can be used to enhance nursing education. For instance, GenAI could be used in a simulation exercise to strengthen students’ relational communication skills. Even weaknesses of GenAI could be leveraged as learning opportunities. For instance, an AI tool to detect skin cancer that has been trained using only images of light-skinned people could be used as a case study to highlight bias in healthcare and limitations of AI.

The authors of this article are affiliated with the University of Manchester, the Christie NHS Foundation Trust, and the University of Calgary. The first author, Siobhan O’Connor (RN, PhD), has an h-index of 18, is a senior lecturer, and is an expert in nursing and health informatics. The citation impact of this article is 1.

# data[].attributes.body
O’CONNOR, S. et al., 2023. Artificial intelligence in nursing education 2: opportunities and threats. Nursing Times, 119(11), pp. 1-5. Available from: https://www.nursingtimes.net/roles/nurse-educators/artificial-intelligence-in-nursing-education-2-opportunities-and-threats-09-10-2023/ [Accessed 26 November 2023].

# data[].attributes.body
This peer-reviewed literature review explores the use of ChatGPT and Metaverse AI technology in nursing education, related to the need to train student nurses to be competent in remote patient monitoring. ChatGPT and Metaverse can be used to create realistic, personalized, and interactive simulation scenarios for student nurses in a safe, controlled environment. These tools can build communication skills and prepare student nurses to reduce healthcare disparities by increasing access to care for patients in rural or impoverished areas through remote patient monitoring.

The authors of this article, Manik Sharma and Samriti Sharma, are affiliated with universities in India – DAV University and Guru Nanek Dev University, respectively. Manik Sharma, PhD, has an h-index of 21. The citation impact of the article is zero, while current altmetrics indicate 27 readers. Nurse Education Today’s five-year journal impact factor is 4.5.

# data[].attributes.body
This double-blind peer-reviewed magazine article is a clinical practice discussion regarding the opportunities and threats of artificial intelligence in nursing education. This article is the second in a two-part series, in which Resource 7 is part one. The authors outline many generative AI tools and plug-ins that can be leveraged to reduce the workload of both students and educators through reviewing, evaluating, and creating academic content. Threats of GenAI related to nursing education include digital inequality, dehumanization, and plagiarism detection bias against students whose native language is not English.

The authors of this article are affiliated with King’s College London, the Mid and South Essex NHS Foundation Trust, and the University of Calgary. The first author, Siobhan O’Connor (RN, PhD), has an h-index of 18, is a senior lecturer, and is an expert in nursing and health informatics. The citation impact of this article is 1.

# data[].attributes.body
This annotated bibliography addresses the use of generative artificial intelligence (GenAI) in the context of higher education, specifically nursing education. The intended audience is nurse educators (lecturers, professors, clinical instructors).

This list of resources is intended to help nurse educators understand GenAI, explore its application in nursing education, and gain insight into the digital and GenAI literacy of nursing students. It also includes ideas on how to develop curriculum to build students’ digital literacy skills.

# data[].attributes.body
1. GONCALVES NES, A.A. et al., 2021. Technological literacy in nursing education: a scoping review. Journal of Professional Nursing, 37, pp. 320-334.

2. HEALTH EDUCATION ENGLAND and ROYAL COLLEGE OF NURSING, 2017. Improving digital literacy. [online]. London: RCN. Available from: https://www.rcn.org.uk/Professional-Development/publications/pub-006129#detailTab [Accessed 26 November 2026].

3. HOLT, K.A. et al., 2020. Health literacy, digital literacy and eHealth literacy in Danish nursing students at entry and graduate level: a cross sectional study. BMC Nursing, 19(22), pp. 1-12. Available from: https://doi.org/10.1186/s12912-020-00418-w [Accessed 26 November 2023].

4. LOKMIC-TOMKINS, Z. et al., 2022. Pre-registration nursing students’ perceptions of their baseline digital literacy and what it means for education: a prospective cohort survey study. Nurse Education Today, 111, pp. 1-8. Available from: https://doi.org/10.1016/j.nedt.2022.105308 [Accessed 26 November 2023].

5. O’CONNOR, S., 2023. Artificial intelligence: A new paradigm in nursing education, research and practice [online video]. Sigma Theta Tau International. 10 October 2023. Available from: https://sigma.nursingrepository.org/handle/10755/23546 [Accessed 27 November 2023].

6. REID, L., BUTTON, D. and BROMMEYER, M., 2023. Challenging the myth of the digital native: a narrative review. Nursing Reports, 13(2), pp. 573-600.

7. TERRY, J. et al., 2019. Improving the digital literacy competence of nursing and midwifery students: a qualitative study of the experiences of NICE student champions. Nurse Education in Practice, 34, pp. 192-198.

8. WANG, W. et al., 2017. Handling internet-based health information: improving health information web site literacy among undergraduate nursing students. Journal of Nursing Education, 56(2), pp. 110-114.