An Introduction to the Artificial Intelligence-Driven Technology Adoption in Nursing Education Conceptual Framework: A Mixed-Methods Study

Abstract

<b>Background/Objectives:</b> Technological advancements are revolutionizing nursing education by improving precision, patient outcomes, and learning experiences. There is an urgent need for systematic frameworks to help nurse educators effectively integrate advanced technologies into their teaching methods. This manuscript introduces the Artificial Intelligence-Driven Technology Adoption in Nursing Education (AID-TANE) framework and operationalizes its use through a pilot study with undergraduate nursing students. <b>Methods:</b> The framework was tested through a convergent mixed-methods pre/post-test study design involving 160 senior-level community health nursing students who participated in an AI-driven educational intervention. Quantitative data were collected using the Facts on Aging quiz, while qualitative data were gathered from a reflective survey. Statistical analyses included paired-sample <i>t</i>-tests and a qualitative content analysis. <b>Results:</b> The study revealed a statistically significant increase in learners' knowledge about older adults, with mean scores improving from 33.29 (SD = 5.33) to 36.04 (SD = 6.76) post-intervention (t = 5.05, <i>p</i> < 0.001). The qualitative analysis identified four key themes: communication and understanding, patience and empathy, respect for independence, and challenging stereotypes. <b>Conclusions:</b> This study found that AI-driven educational tools significantly improved nursing students' knowledge about older adults and positively influenced their learning experiences. The findings highlight the need for targeted frameworks like AID-TANE to effectively integrate AI into nursing education, ensuring that students are ready for a technologically advanced practice setting.

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