Two forms of critical AI literacy and why they matter for farming communities
Imagine two scenarios. In the first, an existing AI chatbot is integrated into a mobile agricultural extension app. Farmers ask questions and receive instant responses. In the second, something more ambitious occurs: a custom chatbot is built from the ground up using local soil data, climate patterns, and indigenous knowledge.
- Artificial Intelligence
By Chioma Chigozie-Okwum, Ameen Jauhar, and Eliot Jones-GarciaMay 29, 2026
Key takeaways
- Co-design involving farming communities improves the reliability of AI agricultural apps, ensuring they reflect local knowledge instead of external assumptions or biases.
- Critical AI literacy helps farmers question, evaluate, and shape AI tools.
- Development approaches vary, requiring different types of critical AI literacy.
Imagine two scenarios. In the first, an existing AI chatbot is integrated into a mobile agricultural extension app. Farmers ask questions and receive instant responses. In the second, something more ambitious occurs: a custom chatbot is built from the ground up using local soil data, climate patterns, and indigenous knowledge. Both rely on years of extension practice; however, the first is relatively resource-effective and prioritizes rapid deployment through adaptation of a general system, while the second, though more resource-intensive, emphasizes contextual relevance for users and long-term value through locally grounded design.
Both scenarios are unfolding in existing projects now. They reflect decisions agritech teams are making to scale their work. In either case, it is essential to engage the farmers and communities that will be using these apps in participatory or co-design processes in which they collaborate with app developers and provide feedback to ensure the final product can meet real-world challenges.
To participate effectively in the design process, farmers and other stakeholders (e.g., extension officers, agronomists) need critical AI literacy: they should have a basic understanding of how AI apps work, what they can accomplish, and their limitations. But the two scenarios require different kinds of knowledge and AI literacy—an emerging challenge that agritech app designers and development practitioners must take into account as AI extension apps scale up in low- and middle-income countries (LMICs).