Nigerian Nurses’ and Nurse Educators’ Readiness for Technology and Artificial Intelligence in Training and Practice: A Cross-Sectional Survey in Oyo State, Nigeria.
Main Article Content
Keywords
AI, Nursing Technologies, Artificial Intelligence, Digital Literacy, Readiness and Utilisation
Abstract
Background: Globally, AI and technology are being integrated into nursing education and practice, providing students with realistic patient care scenarios for safe, hands-on learning. These technological advancements and AI have also aided Nurses in documentation and data input, allowing nurses to focus more on patient care. However, the adoption of AI and other technologies in Nigeria’s nursing environment is still in its early stages compared to more developed countries.
This study investigates the readiness of Nigerian nurses and nurse educators in Oyo state to integrate technology and Artificial Intelligence (AI) into their training and clinical practice.
Methodology: A quantitative survey design was employed, with 115 registered nurses from Oyo State, Nigeria, participating.
Results: The study reveals moderate levels of technological use, with 73% of respondents having used some form of technology or AI tools in their practice. Perceptions towards AI integration were predominantly positive, with over 95% agreeing on its potential to improve healthcare delivery and patient outcomes. Readiness to utilise AI was high, with almost all respondents (97.4%) ready to utilise AI tools in practice. Key barriers to adoption included unavailability of necessary technologies (65.2%), insufficient infrastructure (62.7%), and inadequate training (60.8%). The study found significant associations between previous utilisation of AI, practice designation and readiness to utilise AI, as well as between nursing roles and perceptions of AI integration.
Conclusion: These findings highlight the need to develop and implement comprehensive AI training programs for Nurses, focusing on hands-on experience and understanding of AI applications in patient care. Given the high level of readiness, tailored training programs can help close the gap created by inadequate training.
References
Abou Hashish, E. A., & Alnajjar, H. (2024). Digital proficiency: Assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students. BMC Medical Education, 24(1), 508. https://doi.org/10.1186/s12909-024-05482-3
Abuzaid, M. M., Elshami, W., & Fadden, S. M. (2022). Integration of artificial intelligence into nursing practice. Health and Technology, 12(6), 1109–1115. https://doi.org/10.1007/s12553-022-00697-0
Aderibigbe, A., Ohenhen, P., Nwaobia, N., Gidiagba, J., & Ani, E. (2023). Artificial Intelligence in Developing Countries: Bridging the Gap Between Potential and Implementation. Computer Science & IT Research Journal, 4(3), 185–199. https://doi.org/10.51594/csitrj.v4i3.629
Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A. (2023). A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus, 15(10), e46454. https://doi.org/10.7759/cureus.46454
Akintayo, O. (2024, August 14). Full Document On National Policy On Health Workforce Migration. Channels Television. https://www.channelstv.com/2024/08/14/full-document-on-national-policy-on-health-workforce-migration/
Akwaowo, C. D., Sabi, H. M., Ekpenyong, N., Isiguzo, C. M., Andem, N. F., Maduka, O., Dan, E., Umoh, E., Ekpin, V., & Uzoka, F.-M. (2022). Adoption of electronic medical records in developing countries—A multi-state study of the Nigerian healthcare system. Frontiers in Digital Health, 4, 1017231. https://doi.org/10.3389/fdgth.2022.1017231
Al Wael, H., Abdallah, W., Ghura, H., & Buallay, A. (2024). Factors influencing artificial intelligence adoption in the accounting profession: The case of public sector in Kuwait. Competitiveness Review: An International Business Journal, 34(1), 3–27. https://doi.org/10.1108/CR-09-2022-0137
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M. A., Ag Ahmed, M. A., Fleet, R., & Fortin, J.-P. (2020). Artificial intelligence in health care: Laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Globalization and Health, 16(1), 52. https://doi.org/10.1186/s12992-020-00584-1
Alnasib, B. N. M. (2023). Factors Affecting Faculty Members’ Readiness to Integrate Artificial Intelligence into Their Teaching Practices: A Study from the Saudi Higher Education Context. International Journal of Learning, Teaching and Educational Research, 22(8), Article 8. https://ijlter.org/index.php/ijlter/article/view/8206
Alruwaili, M. M., Abuadas, F. H., Alsadi, M., Alruwaili, A. N., Elsayed Ramadan, O. M., Shaban, M., Al Thobaity, A., Alkahtani, S. M., & El Arab, R. A. (2024). Exploring nurses’ awareness and attitudes toward artificial intelligence: Implications for nursing practice. DIGITAL HEALTH, 10, 20552076241271803. https://doi.org/10.1177/20552076241271803
Al-Sabawy, M. (2023, January 1). Artificial Intelligence in Nursing: A study on Nurses’ Perceptions and Readiness. 1st International and 6th Scientific Conference of Kirkuk Health Directorate, Kirkuk, Iraq.
Austin, S. I. (1999). Baccalaureate Nursing Faculty Performance of Nursing Computer Literacy Skills and Curriculum Integration of These Skills Through Teaching Practice. Journal of Nursing Education, 38(6), 260–266. https://doi.org/10.3928/0148-4834-19990901-06
Booth, R. G., Strudwick, G., McBride, S., O’Connor, S., & Solano López, A. L. (2021). How the nursing profession should adapt for a digital future. The BMJ, 373, n1190. https://doi.org/10.1136/bmj.n1190
Borges do Nascimento, I. J., Abdulazeem, H., Vasanthan, L. T., Martinez, E. Z., Zucoloto, M. L., Østengaard, L., Azzopardi-Muscat, N., Zapata, T., & Novillo-Ortiz, D. (2023). Barriers and facilitators to utilizing digital health technologies by healthcare professionals. Npj Digital Medicine, 6(1), 1–28. https://doi.org/10.1038/s41746-023-00899-4
Chang, C.-Y., Jen, H.-J., & Su, W.-S. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30(8), 3644–3653. https://doi.org/10.1111/jonm.13770
Chen, Y., Wu, Z., Wang, P., Xie, L., Yan, M., Jiang, M., Yang, Z., Zheng, J., Zhang, J., & Zhu, J. (2023). Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. Journal of Medical Internet Research, 25(1), e48249. https://doi.org/10.2196/48249
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Thesis, Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/15192
De Gagne, J. C. (2023). The State of Artificial Intelligence in Nursing Education: Past, Present, and Future Directions. International Journal of Environmental Research and Public Health, 20(6), 4884. https://doi.org/10.3390/ijerph20064884
Derecho, K. C., Cafino, R., Aquino-Cafino, S. L., Isla, A., Esencia, J. A., Lactuan, N. J., Maranda, J. A. G., & Velasco, L. C. P. (2024). Technology adoption of electronic medical records in developing economies: A systematic review on physicians’ perspective. Digital Health, 10, 20552076231224605. https://doi.org/10.1177/20552076231224605
Dorin, M., & Atkinson, H. L. (2024). Getting to know AI: Gaps and opportunities for nurse educators. Teaching and Learning in Nursing, 19(3), 218–224. https://doi.org/10.1016/j.teln.2024.02.013
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023). Artificial Intelligence in Nursing Education: Opportunities and Challenges. Hawai’i Journal of Health & Social Welfare, 82(12), 302–305.
Harmon, J., Pitt, V., Summons, P., & Inder, K. J. (2021). Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review. Nurse Education Today, 97, 104700. https://doi.org/10.1016/j.nedt.2020.104700
Hilty, D. M., Armstrong, C. M., Edwards-Stewart, A., Gentry, M. T., Luxton, D. D., & Krupinski, E. A. (2021). Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review. Journal of Technology in Behavioral Science, 6(2), 252–277. https://doi.org/10.1007/s41347-020-00190-3
Jallad, S. T., Alsaqer, K., Albadareen, B. I., & Al-maghaireh, D. (2024). Artificial intelligence tools utilized in nursing education: Incidence and associated factors. Nurse Education Today, 142, 106355. https://doi.org/10.1016/j.nedt.2024.106355
Jiang, J. (Xuefeng), Qi, K., Bai, G., & Schulman, K. (2023). Pre-pandemic assessment: A decade of progress in electronic health record adoption among U.S. hospitals. Health Affairs Scholar, 1(5), qxad056. https://doi.org/10.1093/haschl/qxad056
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023). Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomedical Materials & Devices (New York, N.y.), 1–8. https://doi.org/10.1007/s44174-023-00063-2
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., & Sabio, J. B. (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, 105945. https://doi.org/10.1016/j.nedt.2023.105945
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., & De Los Santos, J. A. (2023). Student nurses’ attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study. Nurse Education in Practice, 73, 103815. https://doi.org/10.1016/j.nepr.2023.103815
Liang, J., Li, Y., Zhang, Z., Shen, D., Xu, J., Zheng, X., Wang, T., Tang, B., Lei, J., & Zhang, J. (2021). Adoption of Electronic Health Records (EHRs) in China During the Past 10 Years: Consecutive Survey Data Analysis and Comparison of Sino-American Challenges and Experiences. Journal of Medical Internet Research, 23(2), e24813. https://doi.org/10.2196/24813
Loureiro, F., Sousa, L., & Antunes, V. (2021). Use of Digital Educational Technologies among Nursing Students and Teachers: An Exploratory Study. Journal of Personalized Medicine, 11(10), 1010. https://doi.org/10.3390/jpm11101010
Ma, Q., & Liu, L. (2004). The Technology Acceptance Model. Journal of Organizational and End User Computing, 16(1), 59–72. https://doi.org/10.4018/9781591404743.ch006.ch000
McBride, S., Alexander, G. L., Baernholdt, M., Vugrin, M., & Epstein, B. (2023). Scoping review: Positive and negative impact of technology on clinicians. Nursing Outlook, 71(2). https://doi.org/10.1016/j.outlook.2023.101918
McNeil, B. J., Elfrink, V. L., Bickford, C. J., Pierce, S. T., Beyea, S. C., Averill, C., & Klappenbach, C. (2003). Nursing Information Technology Knowledge, Skills, and Preparation of Student Nurses, Nursing Faculty, and Clinicians: A U.S. Survey. Journal of Nursing Education, 42(8), 341–349. https://doi.org/10.3928/0148-4834-20030801-05
Mukerjee, H. S., Deshmukh, G. K., & Prasad, U. D. (2019). Technology Readiness and Likelihood to Use Self-Checkout Services Using Smartphone in Retail Grocery Stores: Empirical Evidences from Hyderabad, India. Business Perspectives and Research, 7(1), 1–15. https://doi.org/10.1177/2278533718800118
Nes, A. A. G., Steindal, S. A., Larsen, M. H., Heer, H. C., Lærum-Onsager, E., & Gjevjon, E. R. (2021). Technological literacy in nursing education: A scoping review. Journal of Professional Nursing, 37(2), 320–334. https://doi.org/10.1016/j.profnurs.2021.01.008
Newbern, V. B. (1985). Computer Literacy in Nursing Education: An Overview. Nursing Clinics of North America, 20(3), 549–556. https://doi.org/10.1016/S0029-6465(22)01898-9
Papadopoulos, P., Soflano, M., Chaudy, Y., Adejo, W., & Connolly, T. M. (2022). A systematic review of technologies and standards used in the development of rule-based clinical decision support systems. Health and Technology, 12(4), 713–727. https://doi.org/10.1007/s12553-022-00672-9
Phuong, J., Ordóñez, P., Cao, J., Moukheiber, M., Moukheiber, L., Caspi, A., Swenor, B. K., Naawu, D. K. N., & Mankoff, J. (2023). Telehealth and digital health innovations: A mixed landscape of access. PLOS Digital Health, 2(12), e0000401. https://doi.org/10.1371/journal.pdig.0000401
Pilcher, J. W., & Bedford, L. (2011). Willingness and Preferences of Nurses Related to Learning With Technology. Journal for Nurses in Professional Development, 27(3), E10. https://doi.org/10.1097/NND.0b013e318217b447
Robinson-Bassey, G. C., & Edet, O. B. (2015). Nursing informatics education and use: Challenges and prospects in Nigeria. Global Journal of Pure and Applied Sciences, 21(2), 171. https://doi.org/10.4314/gjpas.v21i2.9
Rodriguez‐Arrastia, M., Martinez‐Ortigosa, A., Ruiz‐Gonzalez, C., Ropero‐Padilla, C., Roman, P., & Sanchez‐Labraca, N. (2022). Experiences and perceptions of final‐year nursing students of using a chatbot in a simulated emergency situation: A qualitative study. Journal of Nursing Management, 30(8), 3874–3884. https://doi.org/10.1111/jonm.13630
Rony, M. K. K., Akter, K., Debnath, M., Rahman, M. M., Johra, F. tuj, Akter, F., Chandra Das, D., Mondal, S., Mousumi Das, Uddin, M. J., & Rina Parvin, M. (2024). Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care. Journal of Medicine, Surgery, and Public Health, 3, 100113. https://doi.org/10.1016/j.glmedi.2024.100113
Schachner, T., Keller, R., & V Wangenheim, F. (2020). Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review. Journal of Medical Internet Research, 22(9), e20701. https://doi.org/10.2196/20701
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shaimaa, H. M., Shaimaa, A. M. I., & Maaly, Z. M. (2020). Digital Health Literacy (DHL) Levels Among Nursing Baccalaureate Students And Their Perception And Attitudes Toward The Application Of Artificial Intelligence (AI) In Nursing. Egyptian Journal of Health Care, 11(1), 1266–1277. https://doi.org/10.21608/ejhc.2020.274757
Shang, Z. (2021). A Concept Analysis on the Use of Artificial Intelligence in Nursing. Cureus, 13(5), e14857. https://doi.org/10.7759/cureus.14857
Sharma, K. (2024). The Role of Artificial Intelligence in Nursing: Advancements, Challenges, and Future Directions. Tuijin Jishu/Journal of Propulsion Technology, 45(03), Article 03. https://doi.org/10.52783/tjjpt.v45.i03.7378
Sommer, D., Schmidbauer, L., & Wahl, F. (2024). Nurses’ perceptions, experience and knowledge regarding artificial intelligence: Results from a cross-sectional online survey in Germany. BMC Nursing, 23, 205. https://doi.org/10.1186/s12912-024-01884-2
Su, J. S., & Waugh, M. L. (2013). Assessing online learning readiness: A review of the literature and a proposal for a new instrument. World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013, 2350–2359. https://members.aect.org/pdf/Proceedings/proceedings13/2013i/13_36.pdf
Thapa, S., Nielsen, J. B., Aldahmash, A. M., Qadri, F. R., & Leppin, A. (2021). Willingness to use digital health tools in patient care among health care professionals and students at a University Hospital in Saudi Arabia: quantitative cross-sectional survey. JMIR medical education, 7(1), e18590.

Vogel, M., Strina, G., Said, C., & Schmallenbach, T. (2023). The evolution of artificial intelligence adoption in industry. Artificial Intelligence and Social Computing, 72(72). https://doi.org/10.54941/ahfe1003282
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Global Health, 3(4), e000798. https://doi.org/10.1136/bmjgh-2018-000798
Walson, O. B. A., & Okanu-Igwela, J. N. B. (2019, December 2). Issues, Challenges And Prospects In The Use Of Educational Technology For Instructional Delivery In The Management Of 21st University Education In Nigeria. Association for Digital Education and Communications Technology Conference. The Future of Teaching. Learning and Educational Communications Technology in the 21st Century: Issues, Challenges and Prospects, Abuja. https://open.library.okstate.edu/adect/chapter/issues-challenges-and-prospects-in-the-use-of-educational-technology-for-instructional-delivery-in-the-management-of-21st-university-education-in-nigeria/
Wang, X., Fei, F., Wei, J., Huang, M., Xiang, F., Tu, J., Wang, Y., & Gan, J. (2024). Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: A cross-sectional study. Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1433252
Abuzaid, M. M., Elshami, W., & Fadden, S. M. (2022). Integration of artificial intelligence into nursing practice. Health and Technology, 12(6), 1109–1115. https://doi.org/10.1007/s12553-022-00697-0
Aderibigbe, A., Ohenhen, P., Nwaobia, N., Gidiagba, J., & Ani, E. (2023). Artificial Intelligence in Developing Countries: Bridging the Gap Between Potential and Implementation. Computer Science & IT Research Journal, 4(3), 185–199. https://doi.org/10.51594/csitrj.v4i3.629
Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A. (2023). A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus, 15(10), e46454. https://doi.org/10.7759/cureus.46454
Akintayo, O. (2024, August 14). Full Document On National Policy On Health Workforce Migration. Channels Television. https://www.channelstv.com/2024/08/14/full-document-on-national-policy-on-health-workforce-migration/
Akwaowo, C. D., Sabi, H. M., Ekpenyong, N., Isiguzo, C. M., Andem, N. F., Maduka, O., Dan, E., Umoh, E., Ekpin, V., & Uzoka, F.-M. (2022). Adoption of electronic medical records in developing countries—A multi-state study of the Nigerian healthcare system. Frontiers in Digital Health, 4, 1017231. https://doi.org/10.3389/fdgth.2022.1017231
Al Wael, H., Abdallah, W., Ghura, H., & Buallay, A. (2024). Factors influencing artificial intelligence adoption in the accounting profession: The case of public sector in Kuwait. Competitiveness Review: An International Business Journal, 34(1), 3–27. https://doi.org/10.1108/CR-09-2022-0137
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M. A., Ag Ahmed, M. A., Fleet, R., & Fortin, J.-P. (2020). Artificial intelligence in health care: Laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Globalization and Health, 16(1), 52. https://doi.org/10.1186/s12992-020-00584-1
Alnasib, B. N. M. (2023). Factors Affecting Faculty Members’ Readiness to Integrate Artificial Intelligence into Their Teaching Practices: A Study from the Saudi Higher Education Context. International Journal of Learning, Teaching and Educational Research, 22(8), Article 8. https://ijlter.org/index.php/ijlter/article/view/8206
Alruwaili, M. M., Abuadas, F. H., Alsadi, M., Alruwaili, A. N., Elsayed Ramadan, O. M., Shaban, M., Al Thobaity, A., Alkahtani, S. M., & El Arab, R. A. (2024). Exploring nurses’ awareness and attitudes toward artificial intelligence: Implications for nursing practice. DIGITAL HEALTH, 10, 20552076241271803. https://doi.org/10.1177/20552076241271803
Al-Sabawy, M. (2023, January 1). Artificial Intelligence in Nursing: A study on Nurses’ Perceptions and Readiness. 1st International and 6th Scientific Conference of Kirkuk Health Directorate, Kirkuk, Iraq.
Austin, S. I. (1999). Baccalaureate Nursing Faculty Performance of Nursing Computer Literacy Skills and Curriculum Integration of These Skills Through Teaching Practice. Journal of Nursing Education, 38(6), 260–266. https://doi.org/10.3928/0148-4834-19990901-06
Booth, R. G., Strudwick, G., McBride, S., O’Connor, S., & Solano López, A. L. (2021). How the nursing profession should adapt for a digital future. The BMJ, 373, n1190. https://doi.org/10.1136/bmj.n1190
Borges do Nascimento, I. J., Abdulazeem, H., Vasanthan, L. T., Martinez, E. Z., Zucoloto, M. L., Østengaard, L., Azzopardi-Muscat, N., Zapata, T., & Novillo-Ortiz, D. (2023). Barriers and facilitators to utilizing digital health technologies by healthcare professionals. Npj Digital Medicine, 6(1), 1–28. https://doi.org/10.1038/s41746-023-00899-4
Chang, C.-Y., Jen, H.-J., & Su, W.-S. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30(8), 3644–3653. https://doi.org/10.1111/jonm.13770
Chen, Y., Wu, Z., Wang, P., Xie, L., Yan, M., Jiang, M., Yang, Z., Zheng, J., Zhang, J., & Zhu, J. (2023). Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. Journal of Medical Internet Research, 25(1), e48249. https://doi.org/10.2196/48249
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Thesis, Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/15192
De Gagne, J. C. (2023). The State of Artificial Intelligence in Nursing Education: Past, Present, and Future Directions. International Journal of Environmental Research and Public Health, 20(6), 4884. https://doi.org/10.3390/ijerph20064884
Derecho, K. C., Cafino, R., Aquino-Cafino, S. L., Isla, A., Esencia, J. A., Lactuan, N. J., Maranda, J. A. G., & Velasco, L. C. P. (2024). Technology adoption of electronic medical records in developing economies: A systematic review on physicians’ perspective. Digital Health, 10, 20552076231224605. https://doi.org/10.1177/20552076231224605
Dorin, M., & Atkinson, H. L. (2024). Getting to know AI: Gaps and opportunities for nurse educators. Teaching and Learning in Nursing, 19(3), 218–224. https://doi.org/10.1016/j.teln.2024.02.013
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023). Artificial Intelligence in Nursing Education: Opportunities and Challenges. Hawai’i Journal of Health & Social Welfare, 82(12), 302–305.
Harmon, J., Pitt, V., Summons, P., & Inder, K. J. (2021). Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review. Nurse Education Today, 97, 104700. https://doi.org/10.1016/j.nedt.2020.104700
Hilty, D. M., Armstrong, C. M., Edwards-Stewart, A., Gentry, M. T., Luxton, D. D., & Krupinski, E. A. (2021). Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review. Journal of Technology in Behavioral Science, 6(2), 252–277. https://doi.org/10.1007/s41347-020-00190-3
Jallad, S. T., Alsaqer, K., Albadareen, B. I., & Al-maghaireh, D. (2024). Artificial intelligence tools utilized in nursing education: Incidence and associated factors. Nurse Education Today, 142, 106355. https://doi.org/10.1016/j.nedt.2024.106355
Jiang, J. (Xuefeng), Qi, K., Bai, G., & Schulman, K. (2023). Pre-pandemic assessment: A decade of progress in electronic health record adoption among U.S. hospitals. Health Affairs Scholar, 1(5), qxad056. https://doi.org/10.1093/haschl/qxad056
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023). Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomedical Materials & Devices (New York, N.y.), 1–8. https://doi.org/10.1007/s44174-023-00063-2
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., & Sabio, J. B. (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, 105945. https://doi.org/10.1016/j.nedt.2023.105945
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., & De Los Santos, J. A. (2023). Student nurses’ attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study. Nurse Education in Practice, 73, 103815. https://doi.org/10.1016/j.nepr.2023.103815
Liang, J., Li, Y., Zhang, Z., Shen, D., Xu, J., Zheng, X., Wang, T., Tang, B., Lei, J., & Zhang, J. (2021). Adoption of Electronic Health Records (EHRs) in China During the Past 10 Years: Consecutive Survey Data Analysis and Comparison of Sino-American Challenges and Experiences. Journal of Medical Internet Research, 23(2), e24813. https://doi.org/10.2196/24813
Loureiro, F., Sousa, L., & Antunes, V. (2021). Use of Digital Educational Technologies among Nursing Students and Teachers: An Exploratory Study. Journal of Personalized Medicine, 11(10), 1010. https://doi.org/10.3390/jpm11101010
Ma, Q., & Liu, L. (2004). The Technology Acceptance Model. Journal of Organizational and End User Computing, 16(1), 59–72. https://doi.org/10.4018/9781591404743.ch006.ch000
McBride, S., Alexander, G. L., Baernholdt, M., Vugrin, M., & Epstein, B. (2023). Scoping review: Positive and negative impact of technology on clinicians. Nursing Outlook, 71(2). https://doi.org/10.1016/j.outlook.2023.101918
McNeil, B. J., Elfrink, V. L., Bickford, C. J., Pierce, S. T., Beyea, S. C., Averill, C., & Klappenbach, C. (2003). Nursing Information Technology Knowledge, Skills, and Preparation of Student Nurses, Nursing Faculty, and Clinicians: A U.S. Survey. Journal of Nursing Education, 42(8), 341–349. https://doi.org/10.3928/0148-4834-20030801-05
Mukerjee, H. S., Deshmukh, G. K., & Prasad, U. D. (2019). Technology Readiness and Likelihood to Use Self-Checkout Services Using Smartphone in Retail Grocery Stores: Empirical Evidences from Hyderabad, India. Business Perspectives and Research, 7(1), 1–15. https://doi.org/10.1177/2278533718800118
Nes, A. A. G., Steindal, S. A., Larsen, M. H., Heer, H. C., Lærum-Onsager, E., & Gjevjon, E. R. (2021). Technological literacy in nursing education: A scoping review. Journal of Professional Nursing, 37(2), 320–334. https://doi.org/10.1016/j.profnurs.2021.01.008
Newbern, V. B. (1985). Computer Literacy in Nursing Education: An Overview. Nursing Clinics of North America, 20(3), 549–556. https://doi.org/10.1016/S0029-6465(22)01898-9
Papadopoulos, P., Soflano, M., Chaudy, Y., Adejo, W., & Connolly, T. M. (2022). A systematic review of technologies and standards used in the development of rule-based clinical decision support systems. Health and Technology, 12(4), 713–727. https://doi.org/10.1007/s12553-022-00672-9
Phuong, J., Ordóñez, P., Cao, J., Moukheiber, M., Moukheiber, L., Caspi, A., Swenor, B. K., Naawu, D. K. N., & Mankoff, J. (2023). Telehealth and digital health innovations: A mixed landscape of access. PLOS Digital Health, 2(12), e0000401. https://doi.org/10.1371/journal.pdig.0000401
Pilcher, J. W., & Bedford, L. (2011). Willingness and Preferences of Nurses Related to Learning With Technology. Journal for Nurses in Professional Development, 27(3), E10. https://doi.org/10.1097/NND.0b013e318217b447
Robinson-Bassey, G. C., & Edet, O. B. (2015). Nursing informatics education and use: Challenges and prospects in Nigeria. Global Journal of Pure and Applied Sciences, 21(2), 171. https://doi.org/10.4314/gjpas.v21i2.9
Rodriguez‐Arrastia, M., Martinez‐Ortigosa, A., Ruiz‐Gonzalez, C., Ropero‐Padilla, C., Roman, P., & Sanchez‐Labraca, N. (2022). Experiences and perceptions of final‐year nursing students of using a chatbot in a simulated emergency situation: A qualitative study. Journal of Nursing Management, 30(8), 3874–3884. https://doi.org/10.1111/jonm.13630
Rony, M. K. K., Akter, K., Debnath, M., Rahman, M. M., Johra, F. tuj, Akter, F., Chandra Das, D., Mondal, S., Mousumi Das, Uddin, M. J., & Rina Parvin, M. (2024). Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care. Journal of Medicine, Surgery, and Public Health, 3, 100113. https://doi.org/10.1016/j.glmedi.2024.100113
Schachner, T., Keller, R., & V Wangenheim, F. (2020). Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review. Journal of Medical Internet Research, 22(9), e20701. https://doi.org/10.2196/20701
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shaimaa, H. M., Shaimaa, A. M. I., & Maaly, Z. M. (2020). Digital Health Literacy (DHL) Levels Among Nursing Baccalaureate Students And Their Perception And Attitudes Toward The Application Of Artificial Intelligence (AI) In Nursing. Egyptian Journal of Health Care, 11(1), 1266–1277. https://doi.org/10.21608/ejhc.2020.274757
Shang, Z. (2021). A Concept Analysis on the Use of Artificial Intelligence in Nursing. Cureus, 13(5), e14857. https://doi.org/10.7759/cureus.14857
Sharma, K. (2024). The Role of Artificial Intelligence in Nursing: Advancements, Challenges, and Future Directions. Tuijin Jishu/Journal of Propulsion Technology, 45(03), Article 03. https://doi.org/10.52783/tjjpt.v45.i03.7378
Sommer, D., Schmidbauer, L., & Wahl, F. (2024). Nurses’ perceptions, experience and knowledge regarding artificial intelligence: Results from a cross-sectional online survey in Germany. BMC Nursing, 23, 205. https://doi.org/10.1186/s12912-024-01884-2
Su, J. S., & Waugh, M. L. (2013). Assessing online learning readiness: A review of the literature and a proposal for a new instrument. World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013, 2350–2359. https://members.aect.org/pdf/Proceedings/proceedings13/2013i/13_36.pdf
Thapa, S., Nielsen, J. B., Aldahmash, A. M., Qadri, F. R., & Leppin, A. (2021). Willingness to use digital health tools in patient care among health care professionals and students at a University Hospital in Saudi Arabia: quantitative cross-sectional survey. JMIR medical education, 7(1), e18590.

Vogel, M., Strina, G., Said, C., & Schmallenbach, T. (2023). The evolution of artificial intelligence adoption in industry. Artificial Intelligence and Social Computing, 72(72). https://doi.org/10.54941/ahfe1003282
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Global Health, 3(4), e000798. https://doi.org/10.1136/bmjgh-2018-000798
Walson, O. B. A., & Okanu-Igwela, J. N. B. (2019, December 2). Issues, Challenges And Prospects In The Use Of Educational Technology For Instructional Delivery In The Management Of 21st University Education In Nigeria. Association for Digital Education and Communications Technology Conference. The Future of Teaching. Learning and Educational Communications Technology in the 21st Century: Issues, Challenges and Prospects, Abuja. https://open.library.okstate.edu/adect/chapter/issues-challenges-and-prospects-in-the-use-of-educational-technology-for-instructional-delivery-in-the-management-of-21st-university-education-in-nigeria/
Wang, X., Fei, F., Wei, J., Huang, M., Xiang, F., Tu, J., Wang, Y., & Gan, J. (2024). Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: A cross-sectional study. Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1433252
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Apr 2, 2026
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Josiah, B., Shittu, M., Alao, G., Adejumo, C., Adedokun, G., Adehanloye, E., Babalola, N., Olarewaju, V., Idowu, G., Oyegbade, A., Obialor, J., Enebeli, E., Adesuyi, E., & Akingbade, O. (2026). Nigerian Nurses’ and Nurse Educators’ Readiness for Technology and Artificial Intelligence in Training and Practice: A Cross-Sectional Survey in Oyo State, Nigeria. Nigerian Medical Journal, 67(1), 148–170. https://doi.org/10.71480/nmj.v67i1.1214
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