Harnessing Artificial Intelligence To Advance Green Education: Perceptions, Challenges, And Implementation Insights

Abstract

This study explores the literature on Green AI and Sustainable AI using a dual-analysis approach, combining thematic analysis with BERTopic modeling to uncover both broad themes and emerging topics. It identifies three key thematic clusters: (1) Responsible AI for Sustainable Development, which focuses on the integration of sustainability and ethics in AI technologies; (2) Advancements in Green AI for Energy Optimization, which highlights energy efficiency; and (3) Big Data-Driven Computational Advances, which examines AI’s impact on socio-economic and environmental factors. At the same time, BERTopic modeling reveals five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a shift towards incorporating ethical and sustainability issues into AI research. The study uncovers new intersections between Sustainable and Ethical AI and Green Computing, identifying Ethical Healthcare Intelligence and AI Learning Quest as developing areas related to AI’s socio-economic and societal impacts. The research advocates for a holistic approach to AI innovation, emphasizing environmental sustainability and ethical integrity to guide responsible development. This approach aligns with the Sustainable Development Goals, stressing the importance of ecological balance, societal welfare, and responsible innovation, and provides insights for future research and policy actions in the field.