What and How Should Urban Planners Learn in the AI Era? Exploring Urban AI Pedagogy from a Pilot Course in Urban Planning Education

Xiaofan Liang
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Xiaofan Liang: University of Michigan

No g9cps_v1, SocArXiv from Center for Open Science

Abstract: Artificial Intelligence (AI) promises to transform urban planning research, practice, and education, yet few curricula address “Urban AI”. This paper presents the pedagogical design of a pilot Urban AI course and argues for three meta learning goals: applying AI effectively and appropriately in urban challenges, addressing its social, environmental, and governance impacts, and developing normative judgements and professional identities around AI. Pilot teaching produced a knowledge graph connecting essential skills to these goals and a critical framework for AI use and reflection, grounded in analysis of 235 student reflection journals, alongside course evaluations, syllabus materials, and student projects (https://www.xiaofanliang.com/teaching/).

Date: 2026-03-11
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:g9cps_v1

DOI: 10.31219/osf.io/g9cps_v1

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