By the end of this course, you will be able to
Identify where AI credibly supports sustainability across climate action, water, agriculture, cities, waste management, and nature conservation.
Distinguish between traditional AI/ML (prediction and optimisation) and generative AI (content and synthesis) and select techniques appropriately.
Assess the benefits of AI applications against their environmental footprint and apply practical mitigation strategies.
Outline and articulate a concise, ethical action idea related to AI for sustainability.
Course Details
- 2h 14min / 26 lessons
- Last updated: 28/7/2026
- 1 learning resources
- Course completion certificate
Course Content
Course Introduction
SDG Mid-Term Reality Check
Why "Business as Usual" Is Not Enough?
AI as a Double-Edged Sword
Traditional AI vs. Generative AI 1
Traditional AI vs. Generative AI 2
Flood and Climate Risk Forecasting
Emissions Tracking
Energy Optimization & DeepMind Example
AI for Water Systems: Leaks & Maintenance
AI for Agriculture: Field & Farmer Tools
Life From Water Cases: Water Eye & Kenya Assistant
Monitoring Nature: Satellites, Drones & Sensors
AI in Environmental Monitoring Case Studies
NetHope Lens: Should We Use AI Here?
Language Gaps and Inclusion
Ethical Checklist for Sustainability AI
Why Evaluate AI Projects?
Levels of Evaluation
Methods, Tools & Examples
AI's Energy, Water & E-Waste Footprint
Design Principles for Green AI
Readiness Checklist for an AI Pilot
Career Opportunities in AI for Sustainable Development
Action Plan
Conclusion
About this course
This course explains the role of artificial intelligence in supporting sustainability initiatives across climate, water, agriculture, cities, waste management, and biodiversity. Through the course, you will learn about the differences between traditional artificial intelligence and generative artificial intelligence in the context of sustainable development. You will explore how AI is used in predicting climate risks, tracking emissions, improving energy efficiency, managing water and agriculture, and environmental monitoring, supported by real-world case studies. The course also focuses on ethical and environmental dimensions, the AI footprint in energy, water, and electronic waste, as well as evaluation tools, responsible design principles, and building a practical action plan that opens professional opportunities in the field of AI for sustainable development.
Course requirements and prerequisites
No specialist knowledge is required; however, it's preferable to be familiar with sustainability concepts and basic AI terminology to get the ultimate benefit of this course.
Instructor
Meska AI
Egyptian Company Specializing in Artificial Intelligence Education
80 Learners
14 Courses