Nigerian Nurses' and Nurse Educators' Readiness for Technology and Artificial Intelligence in Training and Practice: A Cross-Sectional Survey in Oyo State, Nigeria.
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.
Authors
- Emmanuel Chukwunwike Enebeli (ORCID: https://orcid.org/0000-0002-6535-4884)
- Emily Kofoworola Adehanloye
- Blessing Osagumwendia Josiah (ORCID: https://orcid.org/0000-0001-9042-7195)
- Oluwadamilare Akingbade (ORCID: https://orcid.org/0000-0003-1049-668X)
- Emmanuel O. Adesuyi (ORCID: https://orcid.org/0000-0003-0214-6401)
- Joy Chioma Obialor (ORCID: https://orcid.org/0009-0009-4770-2986)
- Gloria Oluwakorede Alao (ORCID: https://orcid.org/0009-0009-9547-3831)
- Nifemi Tunrayo Babalola
- Gbohunmi Samuel Idowu
- Christy Boluwatife Adejumo
- Grace Ireoluwa Adedokun
- Muhammad Baqir Shittu
- Victor Toyese Olarewaju
- Abosede Peace Oyegbade
Institutions
- Birmingham City University (GB)
- Dalhousie University (CA)
- University of Ilorin (NG)
- University of Jos (NG)
- Leeds Teaching Hospitals NHS Trust (GB)
- Turks and Caicos National Museum (TC)
- Federal Medical Centre (NG)
- Federal Medical Centre (NG)
- Ladoke Akintola University of Technology (NG)
- Nigerian Institute of Medical Research (NG)
- University of Birmingham (GB)
Publication Details
- Journal
- PubMed
- Published
- 2026-08-03
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00