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Transforming geography education: The role of generative AI in curriculum, pedagogy, assessment, and fieldwork

Lee, Jongwon
Cimová, Tereza
Foster, Ellen J.
France, Derek
Krajňáková, Lenka
Moorman, Lynn
Rewhorn, Sonja
Zhang, Jiaqi
Other Titles
Abstract
Generative artificial intelligence (GenAI) represents a major leap forward in AI technology, offering the potential to reshape education in various aspects. This paper explores the transformative potential of GenAI in geography education, focusing on its impacts across curriculum, pedagogy, assessment, and fieldwork, through the lens of the Substitution, Augmentation, Modification, and Redefinition (SAMR) model. In curriculum development, GenAI enables automatic generation and personalization of geographic content. Pedagogical approaches are evolving from text-based instruction to data-driven learning experiences where students analyze geographic phenomena using GenAI tools. Assessment methods are shifting to adaptive evaluation systems with continuous feedback, while fieldwork benefits from real-time data processing and opportunities for global collaboration. Nevertheless, these advancements are accompanied by substantial risks, including challenges such as overreliance on AI, content inaccuracies, biases, and data privacy concerns.
Citation
Lee, J., Cimová, T., Foster, E. J., France, D., Krajňáková, L., Moorman, L., Rewhorn, S., & Zhang, J. (2025). Transforming geography education: The role of generative AI in curriculum, pedagogy, assessment, and fieldwork. International Research in Geographical and Environmental Education, vol(issue), pages. https://doi.org/10.1080/10382046.2025.2459780
Publisher
Taylor & Francis
Journal
International Research in Geographical and Environmental Education
Research Unit
DOI
10.1080/10382046.2025.2459780
PubMed ID
PubMed Central ID
Type
Article
Language
en
Description
This is an Accepted Manuscript of an article published by Taylor & Francis in International Research in Geographical and Environmental Education on 04/02/2025, available online: https://doi.org/10.1080/10382046.2025.2459780
Series/Report no.
ISSN
1038-2046
EISSN
1747-7611
ISBN
ISMN
Gov't Doc
Test Link
Sponsors
Unfunded
Additional Links
https://www.tandfonline.com/doi/full/10.1080/10382046.2025.2459780