3.12 The future of AI and sustainability

Synthesising the journey

This course has navigated the complex and dynamic intersection of Artificial Intelligence and environmental education. We began by establishing the foundational concepts: defining environmental knowledge as a broad spectrum that includes both local, community-based wisdom and quantitative scientific data, and framing AI not as a competitor but as a potential synergistic partner. We then explored the practical opportunities and critical realities of applying AI in environmental projects, highlighting its use in monitoring, prediction, and optimisation while cautioning against over-engineering and data-related pitfalls. From there, we explored the practical toolkit of the digital steward, examining AI-powered citizen science platforms that foster collaborative monitoring and AI techniques for analysing and visualising complex environmental narratives. Through a series of diverse case studies, we witnessed these principles and tools in action, from predicting climate-driven changes in the Arctic to monitoring deforestation in the Amazon and making the threat of climate change personal through generative art.

The double-edged sword and the path forward

As we look to the horizon, it is crucial to hold a clear-eyed view of AI’s dual nature. The technology that offers powerful solutions for environmental monitoring also carries its own significant environmental cost. The massive energy and water consumption of data centres used to train and run large AI models is an unsustainable path. The future of sustainable AI, therefore, will likely depend on a paradigm shift towards smaller, more specialised, and more energy-efficient models – a trend that some experts predict is already underway. This reality reinforces a core message of this course: AI should be used as a scalpel, not a sledgehammer – applied wisely, purposefully, and with a constant awareness of its environmental trade-offs.

Fortunately, there is growing global momentum to steer AI towards a more sustainable trajectory. Major international bodies are recognising both the promise and the peril. The United Nations Environment Programme (UNEP) has been instrumental in launching initiatives like the Coalition for Environmentally Sustainable AI, bringing together governments, tech organisations, and civil society to establish standardised methods for measuring and mitigating AI’s environmental impact. The UN’s broader agenda increasingly highlights AI’s potential as a game-changer for achieving the Sustainable Development Goals (SDGs), while simultaneously calling for global coordination to build safe, inclusive, and accessible AI that reduces bias and security threats. These global conversations provide a vital context, showing learners that their local, community-led efforts are part of a worldwide movement to harness technology for planetary good.

The future is collaborative and requires lifelong learning

If there is one overarching conclusion to be drawn, it is that the future of AI in environmentalism is not purely technological; it is deeply human and profoundly collaborative. The most powerful solutions will emerge not from algorithms in isolation, but from the creative friction and synergy that occurs when we bridge disciplines, cultures, and ways of knowing – when computer scientists work with ethnobotanists, when data analysts partner with community elders, and when local and community-based knowledge is treated as an equal partner to formal scientific data.

This collaborative and rapidly evolving landscape makes one final skill paramount: the commitment to lifelong learning. As was emphasised in the foundational courses of this project, AI is not a static subject to be mastered once, but a dynamic field requiring continuous engagement. The most important competency for the digital steward of the future will be the ability to learn, unlearn, and relearn. It is the commitment to stay curious about new tools and methods, to experiment responsibly and ethically with new technologies, and to continuously reflect on the role of AI in building a more sustainable, just, and resilient world. Platforms designed for knowledge exchange among educators are critical venues for continuing this shared learning journey long after this course is complete. The work of environmental stewardship is ongoing, and in the age of AI, so too is the work of learning how to be its most effective and wisest practitioners.