Caring cannot be prompted: An Autoethnography of Becoming a Nurse Educator in an AI-Enabled ODeL Environment

Abstract

Artificial intelligence (AI) integration into nursing education presents challenges to preserving the human values that define the profession. While AI offers opportunities to enhance teaching and learning, it raises questions about authentic learning, educator identity, and the place of human caring in technology-mediated environments. Guided by Watson's Theory of Human Caring, this analytic autoethnography examined the evolving identity of a nurse educator across intersecting roles as a registered nurse, Open and Distance e-Learning (ODeL) learner, and educator during the emergence of generative AI (GenAI). Personal experiences across these roles traced how the understanding of caring evolved—from recognizing vulnerability in clinical practice to intentionally designing learning experiences that cultivate reflection, educator presence, and authentic engagement in an AI-enabled classroom. The findings demonstrated that AI did not diminish caring; rather, it clarified the distinctly human dimensions of teaching that technology cannot reproduce, including ethical judgment, compassion, meaningful relationships, and reflective practice. 

These experiences further demonstrated that caring could be intentionally expressed through instructional design, authentic assessment, timely feedback, and sustained educator presence, even within ODeL environments. The study argues that the future of nursing education is not defined by the adoption of AI itself but by the capacity of educators to preserve and cultivate human caring while thoughtfully integrating technological innovation. Ultimately, while AI may enhance learning, caring remains the moral foundation of nursing education because it cannot be automated, replicated, or prompted.