Urban Planning AI is not one single technology. It is the use of AI agents inside the planning process itself, and our research treats it as a governance question, not just a technical one.
Urban Planning AI is not one single technology. It is the use of AI agents, in part or in whole, within the planning process itself, to support, augment, automate, or make autonomous different parts of plan preparation and policy formation. This distinguishes it from the broader category of Urban AI, which analyzes cities using tools like machine learning and computer vision but does not necessarily participate in the institutional process of making public planning decisions.
Once AI enters the planning process, it is no longer only helping us understand cities. It participates in shaping public decisions about land use, transportation, infrastructure, zoning, and investment. This is why our research treats Urban Planning AI as a governance question, not just a technical one.
Broader Category
Urban AI
Analyzes cities with machine learning and computer vision. Understands urban systems, but does not itself sit inside the institutional process of making public planning decisions.
Our Focus
Urban Planning AI
Places AI agents inside plan preparation and policy formation itself — supporting, augmenting, automating, or making autonomous parts of that public process.
AI as a Collaboration Tool vs. Urban Planning AI
“Urban Planning AI” and “AI as a collaboration tool” describe two different relationships between AI and the planning process.
What We Do
Urban Planning AI describes the range of roles AI can take on as it becomes more capable, from supporting planners with data and analysis to eventually generating plans with little human input. Our research identifies four phases along this pathway, from our 2024 study in the Journal of Planning Education and Research:
AI has low autonomy. Planners remain dominant, using AI mainly to gather data and identify patterns.
AI simulates and evaluates planning options that planners have proposed, helping identify the strongest ones.
AI semi-automates parts of the planning process and recommends options, under planner supervision.
AI creates plans independently by learning from past experience, with minimal human input.
AI as a Collaboration Tool: A Deliberate Governance Choice
AI as a collaboration tool describes a deliberate choice about how that growing capability should be governed. Rather than treating AI as either a passive tool or a fully autonomous system, our research argues AI should be positioned as a governed co-creator that works alongside planners and communities, with clear mechanisms for human authorization, contestation, and accountability at every stage.
In Short
Urban Planning AI describes how much of the planning task AI is capable of taking on. AI as a collaboration tool describes a deliberate structure that keeps that growing capability paired with human oversight and community accountability, rather than letting technical capability alone determine AI’s role in shaping cities.
Symbiotic Planning Theory and the CORE Framework
Our 2026 paper introduces Symbiotic Planning Theory (SPT), which responds directly to a question the pathway framework left open: once AI begins moving from planning support toward plan-making, what conditions keep that role democratically accountable?
AI must surface the value questions built into a task, turning vague goals like “we want this to be fair” into explicit, enforceable constraints.
Community priorities cannot stay advisory. They must become hard constraints the model is required to satisfy before deployment.
AI’s authorization to operate expires unless renewed through evidence-based human review, preventing algorithmic authority from becoming permanent by default.
Put into Practice through the CORE Framework
These conditions are put into practice through the CORE framework — Collaboration, Options, Refinement, Execution — a cycle that returns to Collaboration each time, so governance is renewed rather than assumed. Figure reproduced from the paper; click to enlarge.
Collaboration
Planners, communities, and agencies define the problem together and set the terms for AI’s involvement before any model runs.
Options
AI generates alternative solutions, not a single final answer, making trade-offs visible rather than hidden inside an algorithm.
Refinement
Proposals are reviewed, challenged, and revised. Humans hold final authority to accept, modify, or reject any AI-generated option.
Execution
Implementation is treated as the start of ongoing, monitored governance, not the end of the planning process.
Case Study: Micromobility in Gainesville
Share of citywide scooter rides, by area
Equity zone
[p]%
Near campus
[p]%
This framework was illustrated through a case study of shared micromobility deployment in Gainesville, Florida, where a vendor met a formal equity rule (deploying scooters in lower-income “equity zones”) while still producing deeply unequal outcomes: those zones generated only about 3% of citywide rides, while roughly 84% of rides began near campus. The research reframed this as a governance problem, not simply a compliance failure, and used AI-driven analysis alongside structured community engagement to redesign the deployment approach around binding equity constraints rather than procedural compliance alone.
Why This Matters
AI can expand what planners are able to analyze, simulate, and test. But without governance, that same capability can shift meaningful decisions into systems that communities, and even decision-makers themselves, cannot easily review or challenge.
Our research in this area works to keep that expanding capability paired with clear mechanisms for human authorization, transparency, and accountability.
References
2024
The Pathway of Urban Planning AI: From Planning Support to Plan-Making
Peng, Z.-R., Lu, K.-F., Liu, Y., & Zhai, W. (2024). Journal of Planning Education and Research, 44(4), 873–885.
