Course overview
A practical pathway built around a clear outcome.
Use AI tools responsibly for learning, communication and enterprise work while checking evidence.
Design a safe, documented AI-assisted workflow for an agricultural use case.
Digital Agriculture and Technology
Responsible AI for African Agriculture
Use AI tools responsibly for learning, communication and enterprise work while checking evidence.In development
Academic and practical requirements
Requirements are stated separately.
- Assessment
- Assessment plan under academic review
- Practical requirement
- Practical requirements to be confirmed
- Entry requirements
- Programme-specific requirements will be published before applications open.
Quality record
Curriculum information
- Curriculum version
- Draft
- Last review
- Not yet reviewed
- Instructor
- Academic team to be confirmed
- Technical reviewer
- Technical reviewer to be confirmed
Entry qualifications
Who this course is for
- Young people, farmers, professionals or enterprise builders working in or entering agriculture.
- Basic English reading ability and access to an internet-enabled phone or computer.
- No prior university qualification is required for Foundation courses.
- Professional and Advanced courses benefit from relevant field, study or enterprise experience.
Learning objectives
What you will build
- Apply prompt design to a defined agricultural context.
- Apply verification to a defined agricultural context.
- Apply responsible use to a defined agricultural context.
- Produce evidence of learning through practical assignments and assessment.
Course structure
Learn. Apply. Demonstrate.
Learn
Lessons and guided resources.
Apply
Exercises linked to an agricultural context.
Demonstrate
Assessment against the stated outcome.