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

Eliot Jones-Garcia; Niyati Singaraju; Kristin Davis; Jawoo Koo

doi:10.37433/aad.v7i2.633

Authors

Eliot Jones-Garcia International Food Policy Research Institute, Washington DC, USA https://orcid.org/0000-0003-0483-2368
Niyati Singaraju International Rice Research Institute, Philippines https://orcid.org/0009-0001-2521-086X
Kristin Davis International Food Policy Research Institute, Washington DC, USA https://orcid.org/0000-0001-9604-921X
Jawoo Koo International Food Policy Research Institute, Washington DC, USA https://orcid.org/0000-0003-3424-9229

DOI:

https://doi.org/10.37433/aad.v7i2.633

Keywords:

SDG 5: Gender Equality, participatory design, evaluation benchmarks, SDG 2: Zero Hunger

Abstract

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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