A New Model for Youth-Driven Community Change: Exploratory Testing of Artificial Intelligence-Supported Citizen...

Area of Science:

• Environmental Health
• Citizen Science
• Artificial Intelligence

Background:

• Generative AI (artificial intelligence) is increasingly used in community settings, but its role in youth-led participatory frameworks is understudied.
• Large language models and AI image tools offer potential for structured feedback and visual prototyping.
• Risks include output variability, feasibility gaps, and potential displacement of youth agency.

Purpose of the Study:

• To examine generative AI tools (GPT model feedback and AI image transformation) as deliberative and visualization supports.
• To assess their function within a youth-led citizen science intervention for environmental health in Cartagena, Colombia.

Main Methods:

• Exploratory action research with preparation and implementation phases.
• Iterative testing of SecureGPT and DALL-E/Adobe Photoshop AI with a 3-3-5 prompt format.
• Facilitator-mediated AI use with 12 adolescent citizen scientists in a real-time, group review process.

Main Results:

• Structured GPT prompts enhanced critical analysis and intervention feasibility.
• AI image tools (Adobe Photoshop AI) generated plausible prototypes aiding discussion, but required careful framing to manage feasibility gaps.
• Facilitator guidance and prompt refinement were crucial for managing AI output variability and specificity.

Conclusions:

• Generative AI tools functioned as supports, not decision-makers, in a facilitated participatory context.
• A workflow involving structured prompting, real-time review, and oversight helps refine feasible solutions and stakeholder communication in citizen science.