Human-AI Collaboration in Agricultural Extension: Reconfiguring Extension-Agent Roles in the Generative AI Era
L. Raja *
Department of Agricultural Extension Education, BJR Agricultural College, Sircilla, Professor Jayashankar Agricultural University, Rajendranagar, Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
Generative artificial intelligence (GenAI), especially large language models (LLMs), is entering agricultural extension at a point when advisory systems are already being reshaped by digitalisation, pluralistic service provision and data-intensive agriculture. This critical narrative review examines how human-AI collaboration changes the work, authority and accountability of extension agents rather than treating GenAI as a direct substitute for human advisory labour. Literature published from 1 January 2000 to 4 July 2026 was considered, with earlier foundational studies retained where necessary to interpret human-automation relationships. Evidence was integrated across agricultural extension, digital agriculture, human-AI collaboration, trust, data governance and emerging empirical evaluations of LLM-enabled agricultural advice. The strongest current evidence supports GenAI as an augmentation layer for information retrieval, drafting, translation, question triage and first-line decision support. Field and expert-evaluation studies also show that model outputs can be technically useful, yet remain vulnerable to local agronomic mismatch, incomplete contextualisation, variable expert ratings and accountability concerns. Evidence that GenAI independently improves long-term farm productivity, income, resilience or equity remains limited. The review therefore argues for a shift from an information-delivery conception of extension work to a bounded-delegation model in which extension agents act as evidence curators, agroecological contextualisers, trust calibrators, facilitators of social learning, data stewards and accountable escalation points. Organisational design is as important as model capability: retrieval-grounded knowledge bases, risk-tiered task allocation, auditability, feedback loops, professional training and public-interest data governance are required to prevent automation bias, deskilling and exclusion. Human-AI collaboration can strengthen extension where it redistributes routine cognitive work towards machines while protecting the relational, interpretive and institutional functions that make advice locally actionable and socially legitimate.
Keywords: Agricultural advisory services, digital extension, generative artificial intelligence, hybrid intelligence, large language models, responsible AI, smallholder agriculture, technology-mediated learning