What this is
Urban AI is a 16-hour online, non-credit learning experience at Michigan State University. It is open to students from every major, and it assumes no background in planning, AI, GIS, coding, or data science.
Cities decide things constantly — where a bus line goes, which buildings get inspected, whose flood risk gets funded. Those decisions increasingly pass through AI systems. This course teaches you to work inside that process: frame an urban problem, use approved AI tools to analyze it, test more than one scenario, audit what the AI told you, and communicate a recommendation that a non-technical stakeholder can act on and contest.
You will finish with an Urban AI Decision-Support Brief — a real artifact for a real urban issue, built by your team, with every AI use documented.
8 meetings, 2 hours each
Twice a week for four weeks. Each meeting pairs a concept class with a hands-on lab and a team breakout.
Hands-on every session
Every lab runs in your browser. Nothing to install, no coding required, whatever your major.
A real decision brief
Work with an interdisciplinary team on an actual urban issue and present your recommendation in week four.
The four sections
Section 1 · Urban analytics and AI concepts. What urban analytics has always done, what smart cities added, what AI genuinely changes — and the vocabulary of machine learning, explained once, in plain language.
Section 2 · Models and cases. Site analysis, simulation, optimization, and scenario testing — then labs that let you do each one: spatial clustering and time series.
Section 3 · Decision support systems. The five DSS categories, deployed cases examined end to end, and how to design an AI-enhanced workflow with the human checkpoints in the right places.
Section 4 · LLMs and ethics. Text mining on community voice, grounding a language model in real planning documents, and treating bias, privacy, transparency and accountability as design requirements.
What you will be able to do
- Define Urban AI and AI-driven decision support systems, and explain how they support evidence-based decisions.
- Describe how planning decisions get made — framing, stakeholders, criteria, alternatives, tradeoffs, judgment.
- Use approved AI tools responsibly for urban analytic tasks.
- Evaluate AI outputs for accuracy, bias, uncertainty, missing context, privacy risk, and relevance.
- Design a human-centered decision-support workflow with transparent assumptions and real human review.
- Produce a decision-support brief that demonstrates critical, ethical, and creative AI use.
If you have never opened a spreadsheet formula, you can complete this course. If you write Python for a living, the sensitivity analyses and the bias audits will still give you something to argue with.
Offered by
- Michigan State University MSU AI-Ready Initiative
- School of Planning, Design and Construction Urban and Regional Planning — Dr. Si Chen, Principal Investigator
- Department of Computational Mathematics, Science and Engineering Dr. Jie Hou, Co-Principal Investigator
- Pulte Institute for Global Development University of Notre Dame — Dr. Yijia Wen, Co-Instructor
The syllabus
The full syllabus — learning outcomes, the week-by-week schedule, what is expected of you, and the course AI policy — is a five-page PDF.
Get started
Register Full schedule Syllabus (PDF)
Cohort 1 · Fall 2026 · Mon & Wed, 5:30–7:30 PM ET · Oct 28 – Nov 23, 2026
MSU AI-Ready Initiative · Category 3 Non-credit Learning Experience