Generative Machine Learning as a Speculative Design Toolkit for Climate Education

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Teaching climate change outcomes can be difficult due to deeply ingrained political viewpoints. Education can often evolve into debates instead of providing information. Instead, process-based methods like storytelling and games can promote climate education by motivating students to engage in speculative future scenario imagining. This work describes a workshop applying generative machine learning tools to allow students to imagine future climate scenarios and come up with potential solution to climate crises. Participants used Stable Diffusion and Midjourney to envision climate futures, and ChatGPT to suggest climate solutions. This approach uses AI as a speculative design tool to promote engagement for future climate thinking.

Presenter: Ray LC, Assistant Professor (City University of Hong Kong)

Session type: Case Study