article · Journal of Umm Al-Qura University for Medical Sciences
A cross-sectional study investigated artificial intelligence adoption, knowledge, and predictors of use among 676 nursing and midwifery students at the University of Health and Allied Sciences in Ghana. Most students (78.6%) report using artificial intelligence, predominantly relying on ChatGPT. Higher rates of usage were linked to male students and final-year undergraduates. Furthermore, roughly 73.7% of participants exhibited adequate knowledge of artificial intelligence. However, this knowledge was mainly acquired through informal channels such as the internet and general media rather than formal coursework. Statistical models predicting usage and knowledge showed modest discriminative power, highlighting that additional unmeasured factors influence uptake. The findings point to a clear gap in formal educational training alongside demographic disparities, underscoring a requirement for structured and equitable artificial intelligence curricula within nursing and midwifery programmes.
Frontline healthcare education in low-resource settings increasingly intersects with emerging technology, yet students largely navigate artificial intelligence without formal guidance. Understanding how healthcare trainees access and understand these tools helps educators identify training gaps and demographic divides, ensuring future nurses and midwives develop safe, equitable, and standardised digital competencies before entering clinical environments.
The abstract highlights early-stage observational research rather than a commercial product or direct technological application. However, the findings reveal a demand for structured educational solutions, identifying healthcare students and training institutions as potential users for formalised artificial intelligence curricula, instructional software, and digital learning platforms tailored to low-resource settings.
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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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DOI: 10.1007/s44361-026-00053-1
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