Improving Nursing Students' Learning Outcomes in Neonatal Resuscitation: A Quasi-Experimental Study Comparing AI-Assisted Care Plan Learning With Traditional Instruction.

Abstract

Aim: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation.

Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction. The control group underwent traditional training consisting of lectures and skill demonstrations, while the experimental group underwent 4 weeks of training utilising an AI-based care plan learning approach. Neonatal resuscitation knowledge tests and student information questionnaires were used for pre- and post-test assessments.

Results: When compared to the control group, the AI-based care plan group demonstrated noticeably greater learning achievement in newborn resuscitation. While the two groups' pre-test results were comparable, the AI-based education group's post-test results were noticeably higher than those of the traditional education group. Furthermore, most of the students had favourable opinions on AI applications and acknowledged their advantages for the nursing field.

Conclusion: The study's conclusions highlight the benefits of incorporating AI technology into nursing education and highlight how it might improve student learning outcomes for vital competencies like newborn resuscitation.