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Asking the right question: Toward a research agenda for responsible GAI in agricultural extension

  • Publication | 2026
Global Food and Nutrition Security

This study explores how generative AI (GAI) tools for agricultural extension can be designed and evaluated more responsibly. While current GAI systems offer scalable, personalized advice, they often ignore the lived realities of smallholder farmers—especially women—by relying on generic datasets and rigid evaluation metrics. We investigate three complementary methods: adversarial testing to expose gendered and contextual blind spots in model outputs; deliberative stakeholder engagement using the C-H-A-T framework, which focused on Collective knowledge, Human insight, Augmentation, and Trust, to surface value tensions and design trade-offs; and field-level insights from extension officers to uncover trust-building, diagnostic reasoning, and social intelligence absent from static GAI interactions. Together, these approaches reveal that responsible GAI requires more than technical accuracy. It demands participatory design processes that foreground user realities, surface stakeholder assumptions, and account for social and institutional context. We recommend developing gender-responsive benchmarks, embedding reflexive, participatory design methods, and modeling advisory reasoning based on real-world extension practice. The findings contribute to a growing agenda for responsible AI development—highlighting the importance of aligning GAI tools not only with technical goals, but with the social, cultural, and political contexts in which they operate.

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Authors
Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; and Koo, Jawoo
Geographic coverageGlobal
Originally published

18 Mar 2026

Related organisation(s)CGIAR - Consortium of International Agricultural Research Centers
Knowledge service | MetadataGlobal Food and Nutrition Security | Research and Innovation | Agricultural extension servicesSmallholder farmer
Digital Europa Thesaurus (DET)gender equalityhungerSustainable development goalsartificial intelligencewomanevaluation method
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