AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism

Environmental journalism is vital for raising awareness of ecological crises and supporting evidence-based policy, yet traditional methods suffer from delays, limited scalability, and lack of coverage in under-monitored regions. This paper introduces the Artificial Intelligence Journalism Integration Model (AIJIM), a conceptual and transferable theoretical model that structures real-time, AI-supported environmental journalism workflows. AIJIM combines citizen-sourced image data, automated hazard detection, dual-level validation (visual and textual), and AI-generated reporting. Validated through a pilot study in Mallorca, AIJIM achieved significant improvements in reporting speed and accuracy, while maintaining transparency and ethical oversight through Explainable AI (XAI), GDPR compliance, and community review. The model demonstrates high transferability and offers a new benchmark for scalable, responsible, and participatory journalism at the intersection of environmental communication and artificial intelligence.

@article{tiltack2025_2503.17401,
  title={ AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism },
  author={ Torsten Tiltack },
  journal={arXiv preprint arXiv:2503.17401},
  year={ 2025 }
}