From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa

Artificial Intelligence (AI) offers new opportunities to improve the efficiency, accessibility, and inclusiveness of e-learning in agricultural and climate research. This InfoNote examines how AI tools can accelerate content development and strengthen capacity-building initiatives across Africa, dra...

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Main Authors: Smith Ruiz, Paulina, Grossi, Amanda, Chipunza, Unity
Format: Brief
Language:Inglés
Published: 2025
Subjects:
Online Access:https://hdl.handle.net/10568/177871
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author Smith Ruiz, Paulina
Grossi, Amanda
Chipunza, Unity
author_browse Chipunza, Unity
Grossi, Amanda
Smith Ruiz, Paulina
author_facet Smith Ruiz, Paulina
Grossi, Amanda
Chipunza, Unity
author_sort Smith Ruiz, Paulina
collection Repository of Agricultural Research Outputs (CGSpace)
description Artificial Intelligence (AI) offers new opportunities to improve the efficiency, accessibility, and inclusiveness of e-learning in agricultural and climate research. This InfoNote examines how AI tools can accelerate content development and strengthen capacity-building initiatives across Africa, drawing on the experience of the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM) as a case example. Using the AICCRA-supported course Integrating Gender in Cost-Benefit Analysis of Climate Interventions as a test case, several AI tools—NOLEJ, Khanmigo, and Google’s Learn Your Way—were assessed across four criteria: quality and innovation, accessibility and inclusion, and resource optimization. The analysis highlights that AI can significantly reduce development time and improve learner engagement when used alongside, rather than in place of, human expertise. RUFORUM’s curriculum development process illustrates the potential for integrating AI strategically within existing e-learning workflows while addressing practical challenges such as limited technical capacity, language diversity, and resource constraints. The InfoNote concludes with actionable recommendations for e-learning implementers, including the need to develop guidance on tool selection, promote responsible AI use, and align AI applications with the FAIR and Open Access principles. Together, these insights provide a foundation for African institutions to explore AI-enhanced e-learning as a pathway to more inclusive and adaptive agricultural education.
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spelling CGSpace1778712025-11-14T02:00:26Z From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa Smith Ruiz, Paulina Grossi, Amanda Chipunza, Unity artificial intelligence africa inclusion capacity development-capacity building education universities Artificial Intelligence (AI) offers new opportunities to improve the efficiency, accessibility, and inclusiveness of e-learning in agricultural and climate research. This InfoNote examines how AI tools can accelerate content development and strengthen capacity-building initiatives across Africa, drawing on the experience of the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM) as a case example. Using the AICCRA-supported course Integrating Gender in Cost-Benefit Analysis of Climate Interventions as a test case, several AI tools—NOLEJ, Khanmigo, and Google’s Learn Your Way—were assessed across four criteria: quality and innovation, accessibility and inclusion, and resource optimization. The analysis highlights that AI can significantly reduce development time and improve learner engagement when used alongside, rather than in place of, human expertise. RUFORUM’s curriculum development process illustrates the potential for integrating AI strategically within existing e-learning workflows while addressing practical challenges such as limited technical capacity, language diversity, and resource constraints. The InfoNote concludes with actionable recommendations for e-learning implementers, including the need to develop guidance on tool selection, promote responsible AI use, and align AI applications with the FAIR and Open Access principles. Together, these insights provide a foundation for African institutions to explore AI-enhanced e-learning as a pathway to more inclusive and adaptive agricultural education. 2025-11-01 2025-11-13T14:18:13Z 2025-11-13T14:18:13Z Brief https://hdl.handle.net/10568/177871 en Open Access application/pdf Smith Ruiz, P., Grossi, A., Chipunza, U. 2025. From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa. AICCRA Brief. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA).
spellingShingle artificial intelligence
africa
inclusion
capacity development-capacity building
education
universities
Smith Ruiz, Paulina
Grossi, Amanda
Chipunza, Unity
From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa
title From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa
title_full From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa
title_fullStr From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa
title_full_unstemmed From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa
title_short From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa
title_sort from science to action exploring the potential of artificial intelligence to strengthen climate and agricultural research capacities in africa
topic artificial intelligence
africa
inclusion
capacity development-capacity building
education
universities
url https://hdl.handle.net/10568/177871
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