INTEGRATING ARTIFICIAL INTELLIGENCE IN FORESTRY EDUCATION: A FRAMEWORK FOR GOVERNANCE, INSTITUTIONAL CAPACITY, AND CURRICULUM INNOVATION

Abstract

Artificial intelligence is transforming forestry practice through applications in remote sensing, wildfire prediction, pest detection, and biomass estimation. Yet, the integration of AI into forestry higher education remains fragmented, shaped less by technology than by governance structures, institutional capacity, and curricular design. This study conducts a comparative analysis of five national models including China’s centralized, the United States’ decentralized, Germany’s regionally collaborative, Australia’s crisis responsive, and Brazil’s community focused approaches to examine how policy frameworks influence educational outcomes. We hypothesize that governance models create trade-offs between scale, innovation, equity, and pedagogical depth. Using policy documents, institutional reports, and curricula, we develop two novel tools: a Forestry Specific AI Competence Framework and a Comparative Curriculum Taxonomy. Findings reveal that centralized systems achieve rapid scale but risk superficial depth, while decentralized systems foster innovation yet exacerbate inequality. Collaborative and mission driven models offer more balanced pathways. The study contributes an evidence based hybrid governance model that blends strategic coordination with localized innovation, providing actionable guidance for building an equitable, practice oriented global forestry workforce.