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Please be aware that the content herein has not been peer reviewed.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)
7/7/2024
Current status of experimental activity
Completed
What portfolio does this activity correspond to? If any
Rethinking Social Cohesion in Panama (Portafolio Approach)
What is the frontier challenge does this activity responds to?
Climate Adaptation
What is the learning question(from your action learning plan) is this activity related to?
How can artificial intelligence be used to understand and enhance the drivers of social cohesion in Panama, particularly through participatory scenario planning around climate change impacts?
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
Academia
Please list the names of partners mentioned in the previous question:
Universidad de Panama
Design
What is the specific learning intent of the activity?
The specific learning intent of the activity is to explore how artificial intelligence can be utilized to engage citizens in the process of scenario planning, with a focus on understanding the drivers of social cohesion in Panama. By leveraging AI to generate visual representations of future scenarios related to climate change, the activity aims to foster active participation, gather diverse perspectives, and identify key factors that influence social cohesion within the community.
What is your hypothesis? IF... THEN....
If artificial intelligence is used to facilitate participatory scenario planning around climate change impacts, then it will enhance citizens' understanding of the drivers of social cohesion and promote more active and inclusive participation in democratic processes.
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?
Random assignment
Describe which actions will you take to test your hypothesis:
To test our hypothesis regarding the impact of using generative artificial intelligence in engaging citizens in climate scenario planning, we implemented a structured approach during the workshop. First, participants used Urbanist AI's interactive platform to input their ideas and concerns related to climate scenarios. Second, we processed these inputs using AI algorithms to generate real-time visualizations of how climate scenarios could affect their local environments. Lastly, we conducted a post-workshop survey to measure participant engagement levels and assess their perceptions of the effectiveness of using AI for civic engagement and scenario planning. These actions allowed us to evaluate whether AI-driven visualizations could effectively enhance citizen participation in discussing and planning for climate change impacts.
What is the unit of analysis of this experimental activity?
The unit of analysis for this experimental activity is the individual participants, specifically the students from the Technological University of Panama who are engaged in the participatory workshop. We will focus on their responses, the diversity of perspectives they provide, and their interactions with the AI-generated scenarios. This includes analyzing their feedback, level of engagement, and the themes and patterns that emerge from their contributions during the workshop.
Please describe the data collection technique proposed
Post workshop survey
What is the timeline of the experimental activity? (Months/Days)
1 day workshop
What is the estimated sample size?
50-99
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.
$2000
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?
Strengths:
Engagement: The use of AI-generated images based on participant inputs has shown to significantly increase engagement and participation levels.
Inclusivity: The methodology allows for a diverse range of perspectives to be visualized and considered, which is crucial for understanding complex social issues like climate change and social cohesion.
Innovative Visualization: AI-generated visualizations make abstract and complex ideas more tangible, facilitating better understanding and discussion among participants.
Areas for Improvement:
Technical Accessibility: Ensuring that all participants are comfortable with and have access to the necessary technology (smartphones, QR codes) is crucial. Technical difficulties can hinder participation and data collection.
Contextual Relevance: The AI's ability to generate contextually relevant images based on local realities could be enhanced. This would ensure that the visualizations resonate more with the participants' lived experiences.
Depth of Analysis: While AI can generate images and identify themes, deeper qualitative analysis of the discussions and feedback could provide richer insights.
From design to results, how long did this activity take? (Time in months)
3 weeks
What were the actual monetary resources invested in this activity? (Amount in USD)
The cost of using the platform Urbanist AI
Does this activity have a follow up or a next stage? Please explain
Yes, the same technology will be used in another set of workshops with the Technological University of Panama with a different methodology. All the information should be used in the national HDR.
Is this experiment planned to scale? How? With whom?
So far, this experiment has not been planned to scale since we are testing a methodology (using AI) to engage citizens participation.
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
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 potential of using AI as a technology for citizen engagement and over climate change awareness and action
What were the main obstacles and challenges you encountered during this activity?
understanding the limitations of technology
Who at UNDP might benefit from the results of this experimental activity? Why?
Anyone working on climate action, citizens engagement, and AI for good
Who outside UNDP might benefit from the results of this experiment? and why?
Ministries or local governments working in specific territories that need to present potential impacts of climate change to communities to gain support for actions
Did this experiment require iterations? If so, how many and what did you change/adjust along the way? and why?
This was the first iteration, a second one was done with a different group of student (will upload in separate template)
What advice would you give someone wanting to replicate this experimental activity?
Make sure you test the platform first, take the training course with UrbanistAI
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, anything related to citizen engagement, specially in urban areas.
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?
Yes, definitely, it helped understand challenges and the relation to the use of the technology with the SGDs
What surprised you?
The level of engagement from those young students, and how images could really crystalize and almost make tangible the impacts of climate change in environments that are known to students.
11Sustainable cities and communities
13Climate action
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