The slide features the title "Between Human Agency and Algorithmic Governance: Reframing Urban Planning Education through Artificial Intelligence," which explores the balance between human decision-making and AI-driven systems in reshaping urban planning curricula. It includes a subtitle noting the presentation at the SOAC 2025 Conference in QUT Brisbane, along with the authors' names and institutional affiliations.

Between Human Agency and Algorithmic Governance: Reframing Urban Planning Education through Artificial Intelligence

SOAC 2025 Conference, QUT Brisbane Authors: [Your Name(s)] Affiliations: [Your Institution(s)]

Source: SOAC 2025 Conference at QUT Brisbane

AI is revolutionizing planning education by shifting from traditional methods to data-driven strategies, enabling innovative teaching through simulations and predictive analytics. The slide highlights challenges in balancing algorithmic governance with human agency, motivating the ethical integration of AI into urban planning curricula.

Context & Motivation

• AI transforms planning education from traditional to data-driven approaches.
• Enables innovative teaching via simulations and predictive analytics.
• Highlights challenges in algorithmic governance versus human agency.
• Motivates ethical integration of AI in urban planning curricula.

The slide titled "Research Aims & Questions" outlines key inquiries into AI's role in urban planning and education. It explores how AI reframes human agency in urban planning, the pedagogical models that emerge from AI integration, and the ethical issues in AI-enhanced planning education.

Research Aims & Questions

• How does AI reframe human agency in urban planning?
• What pedagogical models emerge from AI integration?
• What ethical issues arise in AI-enhanced planning education?

Source: SOAC 2025 Conference Presentation

The slide outlines a methodology involving a systematic literature review conducted according to PRISMA 2020 guidelines, which screened and included 45 articles published between 2018 and 2024. It also describes a thematic coding process that employs color-coded nodes to facilitate analysis.

Methodology

!Image

• Systematic literature review following PRISMA 2020 guidelines
• Screened and included 45 articles from 2018-2024
• Thematic coding process using color-coded nodes for analysis

Source: PRISMA statement

The slide outlines key AI technologies in planning education, including Machine Learning for predictive data analysis, GeoAI for geospatial mapping and simulation, Generative AI for creating scenarios, and AR/VR for immersive visualizations. It highlights their applications, such as ML for urban simulations and traffic forecasting, GeoAI for land-use spatial analysis, Generative AI for design and policy alternatives, and AR/VR for virtual site visits and experiential learning in remote areas.

AI Technologies in Planning Education

| • Machine Learning (ML): Predictive algorithms for data analysis (neural network icon).

• GeoAI: Geospatial AI for mapping and simulation (map overlay icon).
• Generative AI: Content creation for scenarios (lightbulb icon).
• AR/VR: Immersive environments for visualization (VR headset icon). | • ML enables predictive modeling for urban simulations and traffic forecasting.
• GeoAI supports spatial analysis in land-use planning.
• Generative AI generates design alternatives and policy scenarios.
• AR/VR facilitates virtual site visits and experiential learning in remote areas. |

The Pedagogical Innovations stats slide highlights key adoption rates in educational methods, with 75% of curricula incorporating dynamic scaffolding through adaptive feedback. It also notes 60% growth in project-based learning enhanced by AI simulations and an 85% preference for human-in-the-loop approaches that improve collaborative outcomes.

Pedagogical Innovations

• 75%: Dynamic Scaffolding Adoption

Adaptive feedback in curricula

• 60%: Project-Based Learning Growth

AI simulations enhance engagement

• 85%: Human-in-the-Loop Preference

Collaborative design boosts outcomes Source: Literature Review 2020-2024

The timeline slide "Emerging Models & Impacts" outlines the evolution of AI integration in urban planning and education from 2018 to 2024. It begins with GeoAI enhancing simulation models in 2018, progresses to machine learning enabling dynamic scenario planning in 2020 and generative AI fostering innovative projects in 2022, and culminates in 2024 with AI-driven pedagogies promoting equity and bias mitigation in city planning.

Emerging Models & Impacts

2018: GeoAI Integration Begins Geospatial AI tools enhance urban simulation and planning education models. 2020: ML Drives Scenario Planning Machine learning flows into pedagogies, enabling dynamic urban foresight exercises. 2022: Generative AI in Projects AI generates scenarios for project-based learning, fostering innovative outcomes. 2024: Impacts Enhance Equity AI pedagogies promote inclusive planning, mitigating biases for equitable cities.

Source: Adapted from Figure 2: AI-Integrated Pedagogies

The slide titled "Global Case Studies" features an image alongside key examples of AI applications in urban planning and related fields from leading institutions worldwide. These include UMich's GeoAI labs for spatial analysis, MIT's generative design in architecture, UCL's ethical AI frameworks, ETH's AR planning simulations, and NUS's smart city projects.

Global Case Studies

!Image

• UMich GeoAI labs for spatial analysis.
• MIT generative design in architecture.
• UCL ethical AI frameworks.
• ETH AR planning simulations.
• NUS smart city projects.

Source: World map of universities

The slide on Ethical Challenges presents key statistics highlighting AI-related issues in urban planning and education. It reports 70% prevalence of AI bias in urban planning datasets, 55% inequity concerns from educators, 65% integrity risks from algorithmic decisions, and 45% deskilling fears among planning professionals.

Ethical Challenges

• 70%: AI Bias Prevalence

In urban planning datasets

• 55%: Inequity Concerns

Reported by educators

• 65%: Integrity Risks

From algorithmic decisions

• 45%: Deskilling Fears

Among planning professionals Source: Table 2, SOAC 2025 Analysis

The slide outlines future trends in AI for urban planning education, including autonomous GIS for real-time simulations, integrating ethics to tackle bias and equity, and curriculum recommendations emphasizing AI literacy, interdisciplinary projects, and human-centered design. It closes with a quote on AI amplifying human potential, a message to embrace AI thoughtfully, and a call to action for collaborating on ethical AI integration in planning education.

Future Trends & Conclusion

**Future Trends in AI for Urban Planning Education

• Autonomous GIS: AI-driven spatial analysis evolving to self-managing systems for real-time urban simulations [Icon: Robot mapping city].
• Ethics as Core Skill: Integrating ethical AI frameworks into curricula to address bias and equity [Icon: Balance scale with code].
• Curriculum Recommendations: Embed AI literacy, interdisciplinary projects, and human-centered design [Icon: Book with AI gears].

Closing Quote: 'AI amplifies human potential in planning' – Adapted from human-centered AI principles.

Closing Message: Embrace AI thoughtfully.

Call to Action: Collaborate on ethical AI integration in planning education today.**

Source: SOAC 2025 Conference Presentation