Bridging the AI gap in nursing and midwifery education: A cross-sectional analysis of predictors of use and knowledge in Ghana

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Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.

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