Sustainable AI: Analyzing the Environmental Impact of Large-Scale Data Systems in Higher Education

Abstract

As higher education institutions increasingly adopt Artificial Intelligence (AI) and large-scale data systems to enhance research, administration, and learning experiences, concerns about their environmental sustainability have emerged. This research examines the environmental impact of AI-driven data systems within higher education, focusing on energy consumption, carbon footprint, and resource utilization. Through a comprehensive literature review and empirical analysis of case studies from various universities, we identify key factors contributing to the environmental costs of these technologies. Additionally, we explore strategies for mitigating their ecological footprint, including energy-efficient computing practices, green data center initiatives, and the implementation of sustainable AI frameworks. Our findings highlight the necessity for higher education institutions to balance technological advancement with environmental responsibility, offering actionable recommendations to promote sustainable AI practices. This study contributes to the growing discourse on sustainable technology adoption in academia, emphasizing the role of educational institutions in leading the transition towards environmentally conscious AI deployment.