Reconceptualizing Agricultural Extension as an AI-Enabled Knowledge Ecosystem: A Theory-Informed IASEE Framework
Abstract
Agricultural advice now circulates through heterogeneous data infrastructures, AI systems and plural knowledge networks. This article asks how extension can be theorised as a governed knowledge ecosystem in which algorithmic intelligence becomes contextual advice and accountable human decisions. The Integrated AI-Driven Smart Agricultural Extension Ecosystem (IASEE) V1.0 is developed through purposive theory synthesis and conceptual model building. Pluralistic advisory services, agricultural innovation systems, socio-technical governance, knowledge creation, decision support and resilience scholarship are compared according to their explanatory roles and boundaries. This is not a systematic literature review or an empirical evaluation. Eight constructs—agricultural data ecosystem, AI-enabled intelligence, knowledge integration, contextual agricultural knowledge, intelligent agricultural extension, adaptive decision support, human–AI collaboration and agribusiness resilience—are organised into four conceptual domains and eight contestable propositions. Governance and feedback condition the proposed transitions. IASEE specifies three distinct transitions: from digital inference to defensible knowledge, from contextual knowledge to facilitated decisions, and from feasible human action to potential resilience. Its claimed contribution lies in these mediating mechanisms and explicit boundary conditions, not in the first use of AI, governance or human-in-the-loop design in extension. Practical implications and limitations: The model gives researchers and extension organisations a way to examine provenance, knowledge disagreement, decision authority and implementation feasibility. Illustrative maize and cocoa situations in Bolaang Mongondow are conceptual thought experiments. No software deployment, field evaluation or resilience effect is claimed; systematic evidence synthesis and empirical testing are reserved for subsequent research.
