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

BACKGROUND: Generative artificial intelligence (AI) systems are increasingly used in health and community settings, yet empirical evidence on how they function within participatory, youth-led action frameworks remains limited. Large language models can provide structured feedback to support planning and critical reflection, and AI-based image transformation can generate realistic visual prototypes to enhance shared understanding. However, risks include output variability, feasibility gaps when AI-generated recommendations or visualizations imply solutions that are not operationally workable, and the potential to displace adolescent voice and agency if AI outputs are treated as authoritative rather than as inputs for collective deliberation. OBJECTIVE: This study examines how 2 generative AI tools-structured feedback using a GPT model and AI-based image transformation-functioned as deliberative and visualization supports within a youth-led citizen science intervention addressing environmental health concerns in El Pozón, Cartagena, Colombia. METHODS: This exploratory action research study included a preparation phase and an implementation phase. During preparation, researchers iteratively tested SecureGPT (a privacy-enhanced version of ChatGPT 4.0) prompt configurations and compared DALL-E with Adobe Photoshop AI for place-based image modification, selecting a fixed prompt format requesting 3 strengths, 3 weaknesses, and 5 reflective questions (3-3-5). During implementation, 12 adolescent citizen scientists completed the Our Voice process. AI use was facilitator-mediated: prompts were co-developed through youth consensus, a facilitator entered prompts and operated tools while youth observed, and outputs were reviewed with the group in real time before use. Data sources included structured field notes, analytic memos, archived prompts and outputs, and session recordings. Analysis was descriptive and process-oriented, examining how AI shaped deliberation, solution refinement, and stakeholder engagement. RESULTS: Structured GPT prompts supported deeper critical analysis and iterative refinement toward more feasible interventions. Model outputs varied in usefulness; role-based prompting often produced redundant responses, and early outputs were occasionally overly generic, requiring facilitator guidance and prompt refinement. The structured 3-3-5 format improved specificity and reduced wordiness. DALL-E did not generate sufficiently realistic place-based modifications, whereas Adobe Photoshop AI, used with iterative prompting and area-selection tools, produced visually plausible prototypes that supported group discussion and stakeholder communication. Highly realistic visualizations also introduced a feasibility gap when depicted infrastructure exceeded operational constraints, requiring explicit framing of images as aspirational prototypes rather than technical designs. CONCLUSIONS: In this facilitated participatory context, generative AI tools served as structured deliberative and visualization supports rather than autonomous decision makers. For participatory action and citizen science researchers, these findings suggest a practical workflow in which structured prompting, real-time group review, and domain-informed oversight can help participants refine feasible solutions, strengthen communication with stakeholders, and document iterative decision-making while managing variability, accuracy, privacy, and feasibility alignment.