Harnessing artificial intelligence for sustainable education: Integrating environmental awareness and SDG alignment in learning systems - Emaan Danish Khan; Danish Mustafa Khan; Hira Wajahat Malik (Education and Development Research, 2026)
The escalating climate crisis demands urgent transformation in how societies educate future generations. Artificial Intelligence (AI) is increasingly positioned as a disruptive force in education, offering the potential to personalize learning, enhance accessibility, and strengthen environmental literacy. However, its deployment raises critical questions about sustainability, ethics, inclusivity, and alignment with the United Nations Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 13 (Climate Action). This study explores how AI can be responsibly leveraged to create sustainable, climate-conscious education systems. Through a mixed-method research design, the study conducts a comprehensive literature review, examines case studies of AI-enabled sustainability education, and organizes online focus groups with educators, EdTech developers, and students. It further proposes a prototype AI-enhanced learning module designed to integrate environmental awareness into mainstream curricula. The findings are expected to highlight the dual role of AI in both advancing and potentially hindering sustainability, depending on its ethical application and energy demands. The paper argues that AI, if deployed within a framework of environmental responsibility, equity, and global collaboration, can empower learners to engage with climate challenges while equipping them with future-ready skills. It offers a practical framework for integrating sustainability education through AI tools, alongside recommendations for policymakers, educators, and technology developers. The study concludes that responsible AI adoption in education can serve as a critical lever in building climate-smart societies, thereby bridging the gap between technological advancement and global sustainability imperatives.