Disclaimer:
Please be aware that the content herein has not been peer reviewed. It consists of personal reflections, insights, and learnings of the contributor(s). It may not be exhaustive, nor does it aim to be authoritative knowledge.
Overview
Prepared by (Name of the experimenter)
Betty Chemier
On date (Day/Month/Year)
6/8/2024
Current status of experimental activity
Completed
What portfolio does this activity correspond to? If any
Social Cohesion
What is the frontier challenge does this activity responds to?
Digitalization
What is the learning question(from your action learning plan) is this activity related to?
How can AI tools be effectively integrated into participatory urban planning processes to enhance community engagement and inclusivity, particularly across different generations?
Please categorize the type that best identifies this experimental activity:
Quasi experimental (Analytical, observations, etc)
Which sector are you partnering with for this activity? Please select all that apply
Public Sector, Civil Society/ NGOs, Academia
Please list the names of partners mentioned in the previous question:
Universidad Tecnologica de Panama, Comunidad de Aldultos Mayores de Betania, Junta Comunal de Betania
Design
What is the specific learning intent of the activity?
To understand how AI tools can enhance participatory urban planning processes, particularly in fostering increased social cohesion and generating innovative urban design solutions from diverse generational perspectives.
What is your hypothesis? IF... THEN....
If we use AI tools to facilitate participatory urban planning workshops, then participants from different generations will engage more effectively in the planning process, increasing social cohesion and contributing diverse and valuable insights that lead to more inclusive and innovative urban design solutions.
Does the activity use a control group for comparison?
No, it does not use a control group
How is the intervention assigned to different groups in your experiment?
Non-random assignment
Describe which actions will you take to test your hypothesis:
Conduct Intergenerational Workshops: Organize a series of workshops involving participants from different age groups, including students from the Technological University of Panama and seniors citizens from the Betania community.
Utilize AI Tools: Use AI tools, such as UrbanistAI, to render participants' urban design ideas in real-time. This will facilitate visual understanding and help bridge generational communication gaps.
Facilitate Collaborative Design Sessions: Encourage participants to co-create urban space designs, using the AI-generated visuals as a common reference point. Facilitate discussions around these designs to capture diverse perspectives and ideas.
Collect Feedback and Observations: Gather qualitative and quantitative feedback from participants about their experiences using AI in the planning process. Observe interactions to assess levels of engagement and collaboration.
Analyze Social Cohesion Indicators: Assess indicators of social cohesion, such as mutual understanding, willingness to compromise, and shared goals, before and after the workshops.
Evaluate Urban Design Solutions: Analyze the quality and inclusivity of the urban design solutions generated, considering how well they reflect the input from all age groups.
What is the unit of analysis of this experimental activity?
Quantitative surveys and qualitative interviews
Please describe the data collection technique proposed
Pre and post workshops surveys
What is the timeline of the experimental activity? (Months/Days)
We conducted 4 workshops over the course of 3 days
What is the estimated sample size?
100-999
What is the total estimated monetary resources needed for this experiment?
Between 1,000 and 9,999 USD
Quality Check
This activity is relevant to a CPD outcome, The hypothesis is clearly stated, This activity offers strong collaboration oportunities, This activity offers a high potential for scaling, This activity has a low risk
Please upload any supporting images or visuals for this experiment.
Please upload any supporting links
What are the estimated non- monetary resources required for this experiment? (time allocation from team, external resources, etc) If any.
Results
Was the original hypothesis (If.. then) proven or disproven?
Proven
Do you have observations about the methodology chosen for the experiment? What would you change?
There are some limitations regarding the extend of the capacity of the AI to adequately render local contextual elements into the renders of the public spaces, a clear expression of that was that the AI used plants that usually don't grow in Panama´s weather
From design to results, how long did this activity take? (Time in months)
2 months
What were the actual monetary resources invested in this activity? (Amount in USD)
Around 2.5K which was to pay for the server capacity of UrbanistAI to render the images for the 4 workshops
Does this activity have a follow up or a next stage? Please explain
Yes, we going to present the results of the design of the public spaces to the local government next month, for them to consider the elements into their urban planning
Is this experiment planned to scale? How? With whom?
This has been brought up to the regional HDR team who has shown interest in publishing this experience into the regional Latam Human Development Report
Please include any supporting images that could be used to showcase this activity
Please add any supporting links that describe the planning, implementation, results of learning of this activity? For example a tweet, a blog, or a report.
Considering the outcomes of this experimental activity, which of the following best describe what happened after? (Please select all that apply)
This experiment led to partnerships, This experiment led to adoption of new ways of working by our partners
Learning
What do you know now about the action plan learning question that you did not know before? What were your main learnings during this experiment?
The experiment demonstrated that integrating AI tools into participatory urban planning can significantly enhance engagement and inclusivity across generations. We learned that AI tools like UrbanistAI not only provide immediate visual feedback, which facilitates creative discussions, but also help bridge communication gaps between younger and older participants by offering a common, tangible reference point.
What were the main obstacles and challenges you encountered during this activity?
The main obstacles we encountered was using the AI platform, at some moment during the workshop the rendering of images would not be timely, and also sometimes the images would come out distorted. This shows that some challenges persist when using emerging technologies.
Who at UNDP might benefit from the results of this experimental activity? Why?
The results of this experimental activity are valuable for multiple UNDP teams, including the national and regional Human Development Report teams, as they provide practical insights into fostering social cohesion and inclusivity through participatory urban planning. The regional Human Development Report team has already recognized the significance of this experiment, indicating that it will be included in the upcoming regional report. Additionally, teams working on governance, digital transformation, and urban development can adapt these findings to enhance participatory processes and address community-level challenges, particularly in fostering trust and collaboration across generations. The governance team has also shown interest in using it in one of their programmes.
Who outside UNDP might benefit from the results of this experiment? and why?
Outside UNDP, several stakeholders have directly benefited from this experiment. The Technological University of Panama, our key partner, gained valuable insights and practical experience from collaborating on this initiative, which they have integrated into one of their flagship programs aimed at bridging generational gaps through knowledge sharing. Additionally, the local government of Betania benefited from the final public space designs, which incorporated the perspectives of both elderly and young residents, providing them with actionable, community-driven solutions for urban improvement. These outcomes demonstrate the experiment’s potential to foster inclusive collaboration and generate sustainable development impacts beyond UNDP.
Did this experiment require iterations? If so, how many and what did you change/adjust along the way? and why?
Yes, we did 4 workshops in total.
What advice would you give someone wanting to replicate this experimental activity?
Test the tool first. Previous upload of pictures into the platform is recommended for faster editing process (it uses less server capacity this way).
Can this experiment be replicated in another thematic area or other SDGs? If yes, what would need to be considered, if no, why not?
Yes, this experiment can be replicated in other thematic areas and SDGs, such as SDG 13 (Climate Action) for community-driven climate resilience planning or SDG 3 (Good Health and Well-Being) for designing inclusive health infrastructure. Key considerations include adapting the methodology to the specific context, ensuring stakeholder engagement in the targeted area, providing support for technological literacy, and allocating resources for facilitation and follow-up to maintain inclusivity and effectiveness.
How much the "sense" and "explore" phases of the learning cycle influenced/shaped this experiment? In hindsight, what would you have done differently with your fellow Solution Mapper and Explorer?
What surprised you?
How much the use of technology can influence the level of engagement of youth.
10Reduced innequalities
11Sustainable cities and communities
13Climate action
Comments
Log in to add a comment or reply.