Climate change has become a global educational priority that requires learners to interpret scientific evidence, understand spatial relationships, and develop sustainable solutions. In this context, geography education plays a key role in fostering climate literacy, spatial thinking, and geographic inquiry. However, traditional fieldwork is constrained by limitations related to time, cost, accessibility, and safety. Although advances in virtual fieldwork and generative artificial intelligence (AI) provide new opportunities to address these challenges, systematic pedagogical approaches for integrating AI into virtual fieldwork remain limited. This study aims to develop a pedagogically grounded framework for AI-supported virtual fieldwork in climate change education and to demonstrate its application through representative learning activities. The study adopts a conceptual design based on a synthesis of contemporary literature on climate change education, geography education, virtual fieldwork, artificial intelligence, spatial thinking, and educational technologies. The proposed framework is structured around seven pedagogical design principles: beginning with authentic climate change problems, promoting spatial evidence-based learning, positioning AI as a cognitive learning partner, placing geographic inquiry at the centre of learning, fostering solution-oriented learning, employing process-oriented assessment, and ensuring the ethical and responsible use of AI. To illustrate the framework, three representative virtual fieldwork activities focusing on glacier retreat, drought, and urban heat islands were developed. The proposed framework provides a pedagogical foundation for strengthening climate literacy, spatial thinking, geographic inquiry, critical thinking, and AI literacy in geography education.

Keywords: climate change education, AI-supported virtual fieldwork, geographic inquiry, spatial thinking, pedagogical design framework

REFERENCES

Ahmed, F. (2024). The digital divide and AI in education: Addressing equity and accessibility. AI EDIFY Journal, 1(2), 12–23.

Baumeister, R.F., & Leary, M.R. (1997). Writing narrative literature reviews. Review of General Psychology, 1(3), 311–320.

Bednarz, S. W., Heffron, S., & Huynh, N. T. (Eds.). (2013). A road map for 21st century geography education: Geography education research. National Geographic Society.

Bransford, J.D., Brown, A.L., & Cocking, R.R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. National Academies Press.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Cliffe, A. D. (2017). A review of the benefits and drawbacks to virtual field guides in today's geoscience higher education environment. International Journal of Educational Technology in Higher Education, 14(1), Article 28. https://doi.org/10.1186/s41239-017-0066-x

Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.

France, D., & Haigh, M. (2018). Fieldwork@40: Fieldwork in geography higher education. Journal of Geography in Higher Education, 42(4), 498–514. https://doi.org/10.1080/03098265.2018.1515187

Fuller, I.C., Edmondson, S., France, D., Higgitt, D., & Ratinen, I. (2006). International perspectives on the effectiveness of geography fieldwork for learning. Journal of Geography in Higher Education, 30(1), 89–101. https://doi.org/10.1080/03098260500499667

Grant, M. J., & Booth, A. (2009). A typology of reviews. Health Information & Libraries Journal, 26(2), 91–108.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Holstein, K., & Doroudi, S. (2021). Equity and artificial intelligence in education: Will “AIEd” amplify or alleviate inequities in education? arXiv. https://arxiv.org/abs/2104.12920

Ibrahimova, S.I., Jabarov, S.H., Aliyeva, N.A., & Aliyev, Y.I. (2021). Study vibrational and structural properties of Cu₃SeTe by Raman spectroscopy and XRD. International Journal of Modern Physics B, 35(27), Article 2150271. https://doi.org/10.1142/S0217979221502714

Intergovernmental Panel on Climate Change. (2023). Climate change 2023: Synthesis report.

Iskandarov, E., Ismayilov, G., Ismayilova, F., & Myltykbaeva, L. (2026). Active boundary layer approach to laminar flow under high-pressure and entrance conditions. Advanced Physical Research, 8(1), 75–85. https://doi.org/10.62476/apr.8175

Jafarov, S. (2023). Synergetic and innovative approaches to education. Revista de Investigaciones Universidad del Quindío, 35(1), 415–430. https://doi.org/10.33975/riuq.vol35n1.1191

Jafarov, S. (2024). Internationalisation and cultural aspects of online learning, training, and research. Pakistan Journal of Life and Social Sciences, 22(2), 6437–6452. https://doi.org/10.57239/PJLSS-2024-22.2.00486

Jafarov, S. (2024). Education Policy of 3rd Generation Universities. Revista De Gestão Social E Ambiental, 18(6), e05690. https://doi.org/10.24857/rgsa.v18n6-007

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kent, M., Gilbertson, D. D., & Hunt, C. O. (1997). Fieldwork in geography teaching: A critical review of the literature and approaches. Journal of Geography in Higher Education, 21(3), 313–332. https://doi.org/10.1080/03098269708725439

Kolb, D. A. (2015). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson Education.

Lambert, D., & Morgan, J. (2010). Teaching geography 11–18: A conceptual approach. Open University Press.

Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.

Maude, A. (2016). What might powerful geographical knowledge look like? Geography, 101(2), 70–76. https://doi.org/10.1080/00167487.2016.12093987

McKenney, S., & Reeves, T. C. (2012). Conducting educational design research. Routledge.

Mirzayev, N.A., Filosofov, D., Mammadov, Kh., De Jésus, M., Karaivanov, D.V., Ponomarev, D., Rakhimov, A., Rozova, I., Rozov, S., & Temerbulatova, N. (2020). Low radioactive NH₄Cl flux. Journal of Instrumentation, 15, Article T05004. https://doi.org/10.1088/1748-0221/15/05/T05004

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

Monroe, M.C., Plate, R.R., Oxarart, A., Bowers, A., & Chaves, W.A. (2019). Identifying effective climate change education strategies: A systematic review of the research. Environmental Education Research, 25(6), 791–812. https://doi.org/10.1080/13504622.2017.1360842

National Research Council. (2006). Learning to think spatially. National Academies Press.

OECD. (2024). Artificial intelligence in education: Shaping the future of learning.

OECD. (2024). The potential impact of artificial intelligence on equity and inclusion in education.

Puentedura, R.R. (2006). Transformation, technology, and education: The SAMR model. http://hippasus.com

Stainfield, J., Fisher, P., Ford, B., & Solem, M. (2000). International virtual field trips: A new direction? Journal of Geography in Higher Education, 24(2), 255–262. https://doi.org/10.1080/713677387

UNESCO. (2021). Learn for our planet: A global review of how environmental issues are integrated in education.

UNESCO. (2023). Guidance for generative AI in education and research.

U.S. Global Change Research Program. (2009). Climate literacy: The essential principles of climate science.