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

Meiyu Li; Shengqian Zhang; Jiaxin Lu

doi:doi:10.11648/j.ajai.20250902.28

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

Meiyu Li, Shengqian Zhang^*, Jiaxin Lu

Published in American Journal of Artificial Intelligence (Volume 9, Issue 2)

Received: 10 September 2025 Accepted: 10 November 2025 Published: 4 December 2025

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Abstract

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.

Published in	American Journal of Artificial Intelligence (Volume 9, Issue 2)
DOI	10.11648/j.ajai.20250902.28
Page(s)	289-296
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Artificial Intelligence (AI), High School Geography, Climate Change Education, Knowledge Graph, Competency Graph, Cognitive-Behavioral Transformation

References

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APA Style

Li, M., Zhang, S., Lu, J. (2025). AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation. American Journal of Artificial Intelligence, 9(2), 289-296. https://doi.org/10.11648/j.ajai.20250902.28

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ACS Style

Li, M.; Zhang, S.; Lu, J. AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation. Am. J. Artif. Intell. 2025, 9(2), 289-296. doi: 10.11648/j.ajai.20250902.28

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AMA Style

Li M, Zhang S, Lu J. AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation. Am J Artif Intell. 2025;9(2):289-296. doi: 10.11648/j.ajai.20250902.28

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@article{10.11648/j.ajai.20250902.28,
  author = {Meiyu Li and Shengqian Zhang and Jiaxin Lu},
  title = {AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation
},
  journal = {American Journal of Artificial Intelligence},
  volume = {9},
  number = {2},
  pages = {289-296},
  doi = {10.11648/j.ajai.20250902.28},
  url = {https://doi.org/10.11648/j.ajai.20250902.28},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20250902.28},
  abstract = {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.},
 year = {2025}
}

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TY  - JOUR
T1  - AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation

AU  - Meiyu Li
AU  - Shengqian Zhang
AU  - Jiaxin Lu
Y1  - 2025/12/04
PY  - 2025
N1  - https://doi.org/10.11648/j.ajai.20250902.28
DO  - 10.11648/j.ajai.20250902.28
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 289
EP  - 296
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20250902.28
AB  - 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.
VL  - 9
IS  - 2
ER  -

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Author Information

Meiyu Li

School of Geographical Sciences and Planning, Nanning Normal University, Nanning, China
Shengqian Zhang

School of Geographical Sciences and Planning, Nanning Normal University, Nanning, China

Contact Email
Jiaxin Lu

School of Geographical Sciences and Planning, Nanning Normal University, Nanning, China

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APA Style

Li, M., Zhang, S., Lu, J. (2025). AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation. American Journal of Artificial Intelligence, 9(2), 289-296. https://doi.org/10.11648/j.ajai.20250902.28

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ACS Style

Li, M.; Zhang, S.; Lu, J. AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation. Am. J. Artif. Intell. 2025, 9(2), 289-296. doi: 10.11648/j.ajai.20250902.28

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AMA Style

Li M, Zhang S, Lu J. AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation. Am J Artif Intell. 2025;9(2):289-296. doi: 10.11648/j.ajai.20250902.28

Copy | Download

@article{10.11648/j.ajai.20250902.28,
  author = {Meiyu Li and Shengqian Zhang and Jiaxin Lu},
  title = {AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation
},
  journal = {American Journal of Artificial Intelligence},
  volume = {9},
  number = {2},
  pages = {289-296},
  doi = {10.11648/j.ajai.20250902.28},
  url = {https://doi.org/10.11648/j.ajai.20250902.28},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20250902.28},
  abstract = {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.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - AI-Empowered Climate Change Education in High School Geography: A Dual-Graph Approach to Cognitive-Behavioral Transformation

AU  - Meiyu Li
AU  - Shengqian Zhang
AU  - Jiaxin Lu
Y1  - 2025/12/04
PY  - 2025
N1  - https://doi.org/10.11648/j.ajai.20250902.28
DO  - 10.11648/j.ajai.20250902.28
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 289
EP  - 296
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20250902.28
AB  - 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.
VL  - 9
IS  - 2
ER  -

Copy | Download