Planning 3.0: AI-Powered Civic Engagement
Instructor information
Dr. José Marichal (he/him/his)
Professor of Political Science
Contact: marichal@callutheran.edu
Course Description
Urban planning is undergoing a decisive paradigm shift from "planning for people" to "planning with people." This course explores Planning 3.0: the integration of large language models (LLMs), generative AI, computer vision, and intelligent automation into the civic engagement process.
Where Planning 2.0 (web-based GIS and crowdsourcing) expanded who could participate, Planning 3.0 deepens how they participate. We will examine how AI lowers barriers to entry, making participation more intuitive and equitable, and enabling planners to synthesize community input at an unprecedented scale.
Course Objectives
By the end of this course, students will be able to:
- Conceptualize Planning 3.0: Understand the evolution from traditional methods to AI-augmented collaborative systems.
- Design Conversational Interfaces: Explore how LLMs can provide 24/7 multilingual access to complex planning documents.
- Synthesize Public Input: Use AI tools to categorize and summarize thousands of public comments while surfacing minority viewpoints.
- Visualize Scenarios with AI: Leverages generative AI and GIS to translate abstract proposals into interactive visual narratives.
- Audit for Equity and Bias: Critically assess AI systems for disparate impacts and implement human-in-the-loop oversight.
Course Assignments
Total Points: 100
- Conversational Planning Liaison (25 Points): Design a prompt structure for an LLM that enables citizens to query a specific zoning or transit document in plain language.
- Input Synthesis Project (25 Points): Use AI tools to analyze a dataset of public comments, identifying key themes and generating a summary report for planning staff.
- Generative Streetscape Simulation (25 Points): Create a series of AI-rendered visual simulations based on community-defined neighborhood preferences.
- Final Equity Audit and Governance Plan (25 Points): Design a transparency and accountability framework for the deployment of an AI engagement platform in a local municipality.
Schedule
Unit 1: The Evolution of Planning (From 1.0 to 3.0)
Tracing the shift from "planning for" to "planning with" people and the role of the GeoWeb.
The limitations of Planning 2.0 and the promise of the "Intelligent Layer."
- Zheng et al., "Urban planning in the era of large language models" (Nature, 2025)
Overview of conversational access, synthesis, simulation, and adaptive engagement.
- Sanchez, Artificial Intelligence for Urban Planning (Chapters 1-2)
Unit 2: Conversational Access to Planning Information
Bringing the planning department to the resident, 24/7 and multilingual.
Democratizing access to technical reports and regulatory filings.
- "AI-assisted participatory design: a study on LLMs in community renewal" (2025)
How AI reduces administrative burden while increasing participation.
Unit 3: Intelligent Synthesis of Community Input
Overcoming the bottleneck of public comments and identifying "Weak Signals."
Using LLMs to ensure diversity of thought in large-scale engagement.
- Schneier & Sanders, Rewiring Democracy (Deliberative Technologies)
Identifying who is missing from the conversation before decisions are made.
Unit 4: Scenario Simulation and Visual Storytelling
Moving from foregone conclusions to interactive exploration of alternatives.
Translating textual preferences into rendered streetscapes.
- Peng, Symbiotic Planning for Urban Futures (Decision Support Systems)
Using AI to show residents the long-term consequences of current preferences.
Unit 5: Continuous, Adaptive Engagement
Personalizing outreach and monitoring community sentiment in real time.
Alerting planners to emerging concerns before they become crises.
- Governing with Artificial Intelligence: The State of Play (OECD, 2025)
Hyper-local engagement via SMS and social media alerting.
Unit 6: Practical Applications and Equity Auditing
Case studies in zoning guidance and disparate impact analysis.
Automating compliance checks and democratizing developer knowledge.
Scanning plans for language patterns and historical precedents that burden specific populations.
Asset-Based Capacity Building: Focusing on a community's inherent capacities rather than deficiencies, keeping ownership and control within the community.
- Yigitcanlar, Urban Artificial Intelligence (Ethics and Perceptions)
- ABCD Guides and Workbooks
- Community Organising Frameworks, Models, and Processes to Improve Health: A Systematic Scoping Review
Unit 7: Ethics, Responsibility, and Governance
Transparency, accountability, and the "Human-in-the-Loop."
Managing the risks of over-simplification and over-reliance on synthesis.
Community Ownership and Broad-Based Participation: Treating community members as "grassroots experts" to achieve long-term acceptability and sustainability.
Ethical Engagement and Respect: Maintaining transparency, doing no harm, and avoiding the exploitation of participants.
Establishing frameworks for what is collected and who has access.
Distributed Leadership (The Snowflake Model): Enabling others and sharing responsibility through interconnected leadership structures.
Organizing Through Shared Practices: Using practices like telling stories, building relationships, structuring teams, strategizing, and acting to build power.
- Larsson et al., Smart City Governance – AI Ethics in a Spatial Context
- Organizing Guide: People, Power, Change - The Commons
- By the People, For the People: Participatory Budgeting from the Bottom Up in North America
Presentation of Final Equity Audits and looking toward Planning 4.0.
Strategic Collaboration and Coalition Building: Coordinating efforts through models like Collective Impact and Rural Development Hubs to tackle root causes.
A Comprehensive, Long-Term Perspective: Acknowledging that sustained reduction of issues requires taking the "long view" to build deep trust and systemic change.