Author
Listed:
- Jussi S. Jauhiainen
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland
Institute of Ecology and the Earth Sciences, University of Tartu, 50409 Tartu, Estonia)
- Sanni Hakanpää
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland)
- Heikki-Pekka Honkasaari
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland)
- Niilas Kivilompolo
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland)
- Matias Kurri
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland)
- Luukas Lehtiranta
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland)
- Mirva Nurminen
(Department of Geography and Geology, University of Turku, 20014 Turku, Finland)
Abstract
Generative AI (GenAI) is increasingly applied in urban planning for text production, visualization, analytics, stakeholder communication, and participatory engagement. Large language models (LLMs) enable the creation of synthetic participants to support the early-stage design, analysis, and testing of participatory tools. This article demonstrates an innovative use of GenAI through synthetic inhabitants and experts in an immersive digital urban planning environment. DigitalTurku serves as a proof-of-concept for an immersive planning tool within an urban digital twin. The case relies on synthetic personas—residents and expert stakeholders—to evaluate how a GenAI-assisted urban platform may shape participation experiences and trust in local urban planning. The findings indicate that synthetic experts expressed a reduced bureaucratic distance, enhanced transparency, and more meaningful participation. However, assessments of tools and digital environment usability varied according to digital skills and demographic characteristics embedded in the personas. The use of synthetic personas helps identify opportunities and challenges in immersive urban planning environments and supports the design of digital tools in smart cities to strengthen human residents’ spatial understanding and experiential engagement in planning processes. The creation of synthetic data and participants is convenient with LLMs. Despite these tools’ limitations, they can play a valuable role in piloting participatory planning processes to support and complement human-based participation.
Suggested Citation
Jussi S. Jauhiainen & Sanni Hakanpää & Heikki-Pekka Honkasaari & Niilas Kivilompolo & Matias Kurri & Luukas Lehtiranta & Mirva Nurminen, 2026.
"Generative AI in Participatory Urban Planning: Synthetic Inhabitants and Experts,"
Land, MDPI, vol. 15(3), pages 1-19, March.
Handle:
RePEc:gam:jlands:v:15:y:2026:i:3:p:407-:d:1875802
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:15:y:2026:i:3:p:407-:d:1875802. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address
(email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.