Abstract:
Background: Healthcare systems contribute significantly to global greenhouse gas emissions, making sustainability competence an essential component of modern nursing education. Although nursing students generally demonstrate positive attitudes toward environmental protection, there remains a gap between awareness and the ability to integrate sustainability considerations into clinical decision-making. Artificial intelligence (AI)-enabled simulation offers an opportunity to bridge this gap by embedding real-time environmental feedback into experiential learning. This study aims to evaluate the feasibility and preliminary effectiveness of an AI-enabled clinical simulation platform designed to enhance carbon footprint literacy and climate-conscious decision-making among nursing students.
Methods: A pilot educational intervention study was conducted using an AI-powered clinical simulation platform. Nursing students participated in a series of interactive virtual clinical scenarios replicating real-world nursing tasks, including patient care planning, medication administration, infection control, and healthcare resource allocation. During each scenario, participants made sequential clinical decisions. An integrated AI engine automatically estimated and displayed the associated carbon footprint of each decision in real time, including energy use, waste generation, and resource consumption, presented through a visual feedback dashboard.
After each decision point, an AI-driven reflective coaching module generated personalized prompts to encourage students to evaluate the environmental impact of their choices, consider alternative low-carbon strategies, and reflect on sustainable healthcare principles. The intervention was delivered through iterative simulation sessions to support progressive learning and reinforcement of climate-informed clinical reasoning.
Results: Preliminary findings indicated improved awareness of healthcare-related carbon emissions and enhanced ability to incorporate environmental considerations into clinical decision-making. Participants reported high engagement with the AI simulation system and perceived the real-time carbon feedback as highly informative and behavior-changing. The iterative learning design supported progressive improvement in sustainability-oriented clinical reasoning.
Conclusions: An AI-enabled clinical simulation platform integrating real-time carbon footprint feedback represents a promising approach for embedding sustainability competencies into nursing education. By combining clinical decision-making with environmental impact visualization, this intervention supports the development of climate-conscious nursing graduates capable of contributing to sustainable healthcare systems. Further large-scale evaluation is warranted to assess long-term educational and behavioral outcomes.
Biography:
Jiayi Xu is a PhD student at the School of Nursing, Sun Yat-sen University, China. Her research focuses on chronic disease management, nursing education, and digital health innovation. She has published nine papers in JCR Q1 journals, including three first-author publications in international peer-reviewed journals such as BMC Nursing. She has participated in international academic conferences, including the Sigma Nursing Conference. Her current research interests include AI-enabled nursing interventions, healthy ageing, and innovative models of care for older adults and individuals with chronic conditions.
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