How do young people learn about climate change? Chances are they ask a major chatbot, like ChatGPT. The big question is—in a world where climate misinformation is easier to come by than climate justice—what will they learn???
In one of our very first AI Learning Labs projects, we are teaming up with The Climate Academy and #semanticClimate to explore how to design a chatbot for accurate information about the climate that draws on reliable science and systemic social analysis.
Climate Academy is an organisation in Europe that offers schools and students an educational framework for learning about the climate crisis with emphasis on what young people ages 16-19 can do to drive change.
#semanticClimate is a citizen science initiative—with many team members in India— that works to “semantify” climate data to make it open, machine-readable, and actionable for the good of the planet. #semanticClimate is building a prototype for The Climate Academy.
Not just any chatbot
The team is as interested in the outputs as the core values that go into design decisions.
“The real challenge isn’t using AI, but in knowing what to trust. And that starts with good data and the right questions,” says Gitanjali Jadav who leads #semanticClimate.
“We aren’t just building a chatbot. We are rethinking how climate knowledge is shared and trusted in the age of AI,” she says.
In addition, The Climate Academy wishes for a chatbot that doesn’t consume huge amounts of energy and water.
“In both climate change and with AI, it is important to have an understanding that gets beyond clichés. Having an understanding of the systems that play a fundamental role in human society is vital,” says Matthew Pye, founder of The Climate Academy.
Together Open Knowledge, #semanticClimate, and Climate Academy will be hosting an online roundtable on May 18 for a variety of practitioners to discuss recommended approaches based on their experiences with climate-friendly AI projects.
We’re all learning
Open Knowledge’s AI Learning Labs are designed to understand how organisations are grappling with AI, and to help develop custom educational resources for people and organisations to learn from each other, region to region, and in different languages.
With tangible, time-limited challenges like this one, our goal is to support ‘learning by doing’ for organisations in various social sectors through collaborative design.
Gitanjali Jadav agrees with this approach. “You don’t understand AI by using it; you understand it by building with it and seeing where it breaks!” she says.
Indeed, part of our collective learning will be to see how the prototype fares when tested by actual students in different countries. What will it take to move past experimentation?
“We learn best when we wrestle with something. This project offers The Climate Academy the chance to learn about AI through problem solving. Given the complexity involved, moments of deeper learning happen when we are problem framing,” says Matthew Pye, a teacher himself.
About
Open Knowledge’s AI Learning Labs is an initiative that aims to experiment with AI, translate knowledge from social sector organisations around the world, and produce public, multilingual AI-literacy resources tailored for organisations addressing similar issues elsewhere.
Together, we will catalyse learning and develop replicable methods to help organisations build AI skills, use AI responsibly, and develop their own AI projects. All resources will be openly available at School of Data.
Join the conversation:
- Open Knowledge Forum
- School of Data community
- Contact our team: info@okfn.org
This project has been made possible thanks to the generous support of the Patrick J. McGovern Foundation (PJMF). We are grateful for our ongoing partnership in promoting digital literacy and investing in AI for the public good. Learn more about its funding programmes here.
