AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation

This study addresses the persistent challenges of "superficial understanding without depth" and "awareness without action" in current high school geography climate change education. To overcome these limitations, this paper develops an AI-enabled teaching framework that integrates knowledge graphs and competency graphs—collectively termed "dual graphs"—to establish a dual mechanism model for driving "cognition–behavior" transformation. At the cognitive level, the knowledge graph presents climate science concepts in a structured format, while generative AI tools and virtual simulation technologies assist students in conducting systematic knowledge construction and immersive learning experiences. This innovative approach transforms abstract climate concepts into tangible understanding, thereby addressing the issue of fragmented and superficial knowledge acquisition. At the behavioral level, competency graphs define clear developmental pathways and actionable indicators, with AI systems providing personalized feedback, quantitative behavioral tracking, and sustained motivation. This structured support facilitates the crucial transition from climate awareness to concrete, sustainable actions, effectively bridging the "awareness-action gap." The paper further elaborates specific teaching application cases, demonstrating how this framework can be implemented in authentic classroom settings. While analyzing its feasibility, the study also addresses practical challenges including hardware requirements, teacher readiness, and evaluation mechanisms. By offering both theoretical foundations and practical implementation pathways, this research contributes to establishing an operable, evaluable, and effective new model for climate change education within high school geography curricula.