Jordan Journal of Nursing Research

Paper Detail

Exploring Artificial Intelligence Literacy among Midwifery Students

Volume 4, No. 3, 2025
(Received: 2025/04/14, Accepted: 2025/06/19)

 Views: 623
 Downloads: 363

Authors:

Suad Jakalat; Roba Bdeir; Rula Al-Rimawi; Sarah Al-ja'freh; Tamador Al-Tarawneh;

Keywords:

Artificial Intelligence, Literacy, Midwifery Students, Jordan.

Abstract:

Background: The study of artificial intelligence (AI) has gained popularity, particularly in the field of education, but the integration of AI in midwifery education is at an early stage, and the   AI literacy levels among midwifery students need to be explored. Purpose: This study aims to explore the AI literacy levels and the associated factors among midwifery students. Methods: A descriptive, cross-sectional, correlational research design guided the study protocol. Data was collected using the Artificial Intelligence Literacy Scale (AILS), the literacy total score was categorized into: low (0-49%, scores < 51), moderate (50-79%, scores 52-58), and high level (80-100%, scores >59).  (AILS) distributed online to 250 midwifery students across different academic years. Setting of the study: One public university in Jordan. Results: more than half of the midwifery student (51%) had a low literacy level towards AI and its technologies, while only 9% had a high literacy, the AI literacy was significantly associated with the academic year (X2 = 9.064, P = 0.05), their awareness about AI (X2 = 9.064, P < 0.001), and frequency of computer use (X2 = 9.320; P < 0.001). Conclusion: This paper provides evidence that literacy levels among students can vary according to the level of academic year, their awareness of AI, and frequency of computer usage. Students with advanced academic years, who is aware of AI, and frequently use AI technology, have a higher literacy level compared to those with medium or low literacy levels. Implications: Integrating AI technologies within the entire curriculum for theory and practice courses is essential. This could be achieved through informing policymakers to plan policies for AI, taking into account funding issues, ethical, inclusive, and equitable use among students, based on up-to-date evidence that aids in the creation of AI education resources supported by pedagogical research and sound methodologies.