A Multidimensional Assessment of Generative AI Models in Theoretical and Clinical Nursing Education

Debanjana Prasad and Pratibha Khosla

Seminars in Medical Writing and Education, 2023, vol. 2, 103

Abstract: Generative Artificial Intelligence (GAI) has rapidly emerged as an advanced technology with the potential to revolutionize various sectors, including education and healthcare. Despite the growing interest in applying GAI models to nursing education, there remains a lack of comprehensive research assessing their multidimensional impact. Research aims to determine the multifaceted impact of GAI on nursing education, focusing on its effectiveness and challenges. A mixed-methods approach was employed, combining qualitative interviews and focus groups with quantitative surveys and pre-and post-test assessments. Research involved nursing students, educators, and clinical practitioners from various institutions and healthcare settings, with a total of 820 participants gathered. Thematic analysis was employed to examine views, experiences, and issues pertaining to GAI in qualitative data. IBM SPSS software version 28 is used for the statistical analysis. Descriptive statistics were used to examine quantitative data to compile survey results and measures of academic achievement, while the findings of the pre- and post-tests were compared using paired t-tests to measure changes in knowledge and clinical skills. The GAI model is to increase student engagement (from 60% to 80%), improvement in personalized learning (from 65% to 87%), and an increase in theoretical knowledge retention (from 70% to 95%). The findings suggest that GAI enhances student engagement, offers personalized learning experiences, and improves theoretical knowledge retention. The research highlights the need for careful integration of GAI into nursing curricula and offers recommendations for addressing the ethical considerations and ensuring effective use in clinical training.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:medicw:v:2:y:2023:i::p:103:id:103

DOI: 10.56294/mw2023103

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