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

Editorial reading guide · Collaboration

How could young people use AI to develop a shared community idea while keeping local knowledge and the final choice in human hands?

Young people collectively examined neighborhood problems while a facilitator entered agreed prompts and helped them critique AI feedback and images. Structured LLM responses offered strengths, weaknesses, and questions, while generated visualizations sometimes misrepresented feasible local changes. The study foregrounds youth deliberation and local expertise rather than allowing the tool to define community priorities.

Study context

12 members of an existing youth ecology group in an under-resourced neighborhood of Cartagena, Colombia

Age / stage: Adolescents; mean age 14 years (SD 1.41)

Read with three questions

1. What decisions did young people make collectively, and what work did the facilitator do?
2. Which AI-generated suggestions or visualizations failed to fit local realities?
3. How would the conclusions change without an established group, expert mediation, or paid tools?

Keep the limits in view

This small exploratory study had no comparison group and involved a cohesive pre-existing group, facilitator-operated tools, researcher observations, and short follow-up. It cannot attribute participation, confidence, or community change to AI. Image feasibility and access costs remained issues, and the implementation was not independent adolescent chatbot use.