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Generative AI use and advisory performance among agricultural extension agents in Benin
Mori W Gouroubera1,2, Alcade C Segnon1,3, Morrisson Gouthon2
1International Center for Tropical Agriculture (CIAT), Dakar, Senegal.
Frontiers in Artificial Intelligence
|June 11, 2026
Summary
Generative Artificial Intelligence (GenAI) improves agricultural advisory services, with workload and pressure driving its adoption. However, extension agents show hesitant engagement due to concerns about trust and accountability.
Area of Science:
- Agricultural Extension
- Artificial Intelligence Governance
- Human-Computer Interaction
Background:
- Generative Artificial Intelligence (GenAI) presents opportunities to enhance agricultural advisory services.
- Limited understanding exists regarding extension agents' engagement with GenAI.
- The Technology Acceptance Model (TAM) provides a framework to analyze technology adoption.
Purpose of the Study:
- To investigate agricultural extension agents' interaction with GenAI in Benin.
- To assess the impact of GenAI on advisory performance using TAM.
- To identify factors influencing GenAI adoption and its consequences.
Main Methods:
- Survey of 240 agricultural extension agents across six districts in Benin.
- Application of Partial Least Squares Structural Equation Modeling (PLS-SEM).
- Analysis of relationships between perceived usefulness, workload, time pressure, attitudes, behavioral intentions, GenAI use, and performance.
Main Results:
- GenAI use positively correlates with improved advisory effectiveness.
- Workload and time pressure are significant motivators for GenAI adoption.
- Perceived usefulness positively influences attitudes and perceived ease of use.
- Attitude negatively impacts behavioral intention, indicating hesitant adoption despite perceived value.
Conclusions:
- GenAI adoption by agricultural extension agents is influenced by a complex interplay of perceived benefits and professional concerns.
- Hesitancy in full reliance on GenAI stems from issues of professional judgment, accountability, and trust in AI outputs.
- Findings offer insights for capacity-building and policy frameworks promoting responsible AI integration in agricultural extension.
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