Bridging Implementation Gaps: The Critical Role of Institutional Ecosystems in AI Integration for Nursing Education
TL;DR
This commentary is deliberately framed as a focused extension of the original study’s individual-level findings to the institutional level, rather than a stand-alone organizational literature review, and its scope has been kept correspondingly concise.
Abstract
This letter is a cohmment on the SAGE Open Nursing article “Nursing Educators” Perspectives on the Integration of Artificial Intelligence Into Academic Settings” (Rony et al., 2025). This qualitative study provides valuable insights into nursing educators’ lived experiences with AI integration in Bangladesh, identifying key themes including perceived benefits, barriers, ethical considerations, educator readiness, and personalized learning potential. While the authors’ use of the Technological Pedagogical Content Knowledge (TPACK) framework effectively captures individual-level factors, I would like to elaborate on the institutional and systemic dimensions that are equally critical for sustainable AI integration in nursing education. In doing so, this letter extends their individual-level TPACK lens to the institutional, governance, and equity structures that determine whether AI can be adopted safely and equitably across nursing programmes, where AI-based tools are already being taken up at pace (Labrague & Al Sabei, 2025). This commentary is deliberately framed as a focused extension of the original study’s individual-level findings to the institutional level, rather than a stand-alone organizational literature review, and its scope has been kept correspondingly concise. The findings underscore that educators’ concerns extend beyond individual competencies to encompass broader institutional ecosystems. The participants’ observations that “there’s no training” and that they are being “handed tools without a manual” (Rony et al., 2025) reflect a systemic gap in implementation infrastructure rather than merely individual skill deficits. From an implementation science perspective, successful technology adoption requires attention to multiple contextual levels: the intervention itself, the inner organizational setting, the outer policy environment, and the individuals involved (Damschroder et al., 2022). Future research and practice should therefore consider developing