Acceptance of AI-driven Educational Technologies Among Nurse Educators: An Embedded Triangulation Study Using the Technology Acceptance Model (ATM)

Abstract

This study examined the acceptance of AI-driven educational technologies among nurse educators using an embedded mixed-methods design guided by the Technology Acceptance Model (TAM). The quantitative phase assessed perceived usefulness (PU), perceived ease of use (PEOU), attitude toward use (ATU), and behavioral intention (BI) among 55 nurse educators, while the qualitative phase explored their lived experiences, perceptions, and challenges. Results revealed high levels of PU, PEOU, ATU, and BI, indicating strong acceptance of AI in teaching practices. No significant differences were found across demographic variables, except for PEOU, which varied by years of teaching experience. Qualitative findings supported and explained these results, highlighting AI’s role in improving teaching efficiency, accessibility of information, and student engagement. However, challenges related to limited training, insufficient resources, and ethical concerns such as overdependence and accuracy were also identified. The integration of findings demonstrated convergence, where qualitative insights validated quantitative patterns and provided contextual explanations. Overall, the study concludes that while nurse educators show readiness to adopt AI technologies, effective integration requires structured institutional support, training, and ethical guidance. A professional development program is recommended to enhance digital competence and promote responsible AI use in nursing education.