From environmental observation to shared narratives through human-AI interaction

A structural bottleneck limits sustainability practices: many people can participate in environmental observation, but far fewer can participate in the synthesis work that turns observations into shared narratives that guide action. We term this disparity "synthesis inequality". Citizen-science programs have expanded public access to data collection, yet data interpretation largely remains concentrated within scientific institutions. Local communities therefore generate observations but often lack the infrastructure to translate them into coherent narratives for decision-making. This paper introduces the AIRE framework, a structured approach to human-AI interaction designed to expand participant-led synthesis while protecting human agency. AIRE operationalizes a relational cycle of four phases (Connect, Reflect, Create, Share) in which generative AI functions are separated into bounded roles that support relational continuity, psychological safety, and synthesis assistance without displacing human interpretive authority. The theoretical foundation draws on polyvagal theory, the zone of proximal development, productive failure, and self-determination theory to establish physiological and developmental preconditions for reflective engagement. Social guardrails, including interrogative scaffolding, modular co-creation interfaces, and human facilitation protocols, ensure that the AI amplifies rather than replaces human meaning-making. The framework is grounded in established data governance principles (OCAP and CARE) and advances operational sovereignty as a practical standard for community control over data, narratives, and attribution. The paper presents testable hypotheses linking framework participation to narrative agency, emotional connection to nature, and intergenerational belonging, and proposes longitudinal methods for empirical validation.