Artificial Intelligence Workforce Development for Data-Driven Governance in Environmental Management

Mangrove ecosystems are critical for coastal resilience, yet their management faces increasing challenges due to rapid environmental change, fragmented data systems, governance limitations, and the inadequate adaptive capacity of key stakeholders. This study presents an AI-powered adaptive learning framework that integrates predictive analytics, knowledge management, and participatory learning to enhance both ecological governance and human capacity development. Machine learning and remote sensing are employed to generate predictive environmental intelligence, enabling early detection of degradation risks, prioritization of restoration zones, and evidence-based policy formulation. Complementarily, knowledge management platforms facilitate the integration of scientific data with local ecological knowledge, while participatory learning mechanisms strengthen stakeholder engagement, legitimacy, and trust in AI-supported decision processes. Empirical evidence from Indonesian mangrove contexts demonstrates improvements in monitoring precision, collaborative problem-solving, and adaptive governance performance. Beyond ecological benefits, the framework contributes to workforce development by fostering digital literacy, analytical competence, strategic thinking, and adaptive leadership aligned with smart governance principles. The findings highlight the transformative potential of AI-enabled learning ecosystems in bridging technological innovation with human-centered governance, offering scalable implications for environmental management, institutional resilience, and sustainable policy implementation. This study provides actionable insights for policymakers, NGOs, practitioners, and academic institutions seeking to institutionalize AI-supported adaptive governance in coastal socio-ecological systems