Abstract
This chapter explores the transformative impact of generative Artificial Intelligence (AI) on geography education through the lenses of the SAMR model, Bloom’s Taxonomy, and the AI-TPACK framework. It examines how AI is reshaping teaching methods, learning experiences, and teacher competencies in geography education. The SAMR model demonstrates AI’s potential to redefine geographical learning experiences, moving beyond mere substitution to enable novel educational approaches. Bloom’s Taxonomy analysis reveals how AI alters students’ cognitive experiences, enhancing higher order thinking skills while potentially risking over-reliance on AI-generated content. The AI-TPACK framework highlights the evolving competencies required for geography teachers in the AI era, emphasizing the need for integrated technical, pedagogical, and content knowledge.
| Original language | English |
|---|---|
| Title of host publication | Springer Geography |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 297-312 |
| Number of pages | 16 |
| DOIs | |
| State | Published - 2025 |
Publication series
| Name | Springer Geography |
|---|---|
| Volume | Part F723 |
| ISSN (Print) | 2194-315X |
| ISSN (Electronic) | 2194-3168 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keywords
- Generative AI
- Geography education
- SAMR model