AI 시대, 경제지리학의 유용성과 경제지리 교육의 존재 이유

초록

This study reexamines the disciplinary relevance of economic geography in the age of AI and, on that basis, proposes a direction for economic geography education. Generative AI is expanding the automation and assistance of structured quantitative tasks in economic geography, including spatial data analysis, location optimization, and regional forecasting; approaches in geographical economics centered on deductive modeling are relatively more exposed to this pressure. At the same time, AI is itself a new phenomenon that economic geography must explain. The spatial concentration of data centers, semiconductor clusters, and AI talent demonstrates that the technology is deeply rooted in particular places, accelerating uneven development. The conceptual resources of economic geography—agglomeration economies, embeddedness, and path dependence —remain effective analytical tools for accounting for these new geographies of inequality. This study conceptualizes the ability to interpret and contextualize phenomena unfolding in economic space through economic-geographical concepts—a capacity distinct from GIS- or geomedia-centered spatial literacy—as “economic-geographical literacy,” and the capacity to pursue and enact spatial justice, grounded in insight into an unequal world, as “critical spatial citizenship,” positioning the former as the epistemological foundation of the latter. Ultimately, the relevance of economic geography in the age of AI lies in its capacity to interpret spatial inequality in context, and cultivating this interpretive capacity as literacy and fostering it into critical spatial citizenship constitute the rationale for economic geography education.

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