Bridging the AI gap in nursing and midwifery education: A cross-sectional analysis of predictors of use and knowledge in Ghana
TL;DR
A high reliance on informal, self-directed AI learning exists among students, revealing a critical gap in formal education and necessitate the urgent integration of structured, equitable AI curricula into Ghana’s nursing and midwifery training programs.
Abstract
Artificial Intelligence (AI) offers transformative potential for healthcare education, yet its adoption among key frontline cadres in low-resource settings remains poorly understood. This study is the first to investigate the predictors of AI usage and knowledge among nursing and midwifery students in Ghana. An analytical cross-sectional study was conducted with 676 students from the University of Health and Allied Sciences, recruited via convenience sampling. A validated questionnaire assessed AI knowledge, usage patterns, and sources of information. Data were analyzed using descriptive statistics, binary logistic regression, and ROC analysis in STATA v17.0. Most participants (78.6%) used AI, with significantly higher odds among males (aOR: 2.33, 95% CI;1.21–4.47, p = 0.011) and final-year students (aOR: 3.05, 95% CI;1.64–5.67, p = 0.001). While 73.7% demonstrated adequate knowledge, acquisition occurred primarily through informal sources (internet/media), with ChatGPT being the dominant tool. Predictive models for AI usage and knowledge demonstrated significant associations but modest discriminative power (AUC range: 0.58–0.63), indicating the role of unmeasured factors. A high reliance on informal, self-directed AI learning exists among students, revealing a critical gap in formal education. Despite strong adoption, significant demographic disparities and a clear “usage-knowledge disconnect” necessitate the urgent integration of structured, equitable AI curricula into Ghana’s nursing and midwifery training programs.