Telling the story of our changing planet

-We are a collaborative community of educators, technologists, and environmental organizations with a shared mission to develop data-based learning resources and tools that support community-based programs serving global classrooms.

-Our resources are based on the transformative learning models and theories found in ecopedagogy, climate justice, and culturally oriented knowledge systems that address current environmental and social challenges. Our programs and resources aim to inspire community participants to be changemakers and critical thinkers and to show how they can use their own data and observations to form plans of action.

Earth Teach Alliance AI generated image. (CC BY-SA)

Community-based Data and Data Equity

Digital inequality excludes disconnected populations—often those most affected by climate change—from contributing data and shaping environmental policy. Climate models and AI-driven environmental monitoring depend on data.

If rural, low-income, or Indigenous areas are digitally excluded, their environmental realities remain invisible in datasets. This invisibility reproduces environmental injustice—because what isn’t measured or mapped isn’t prioritized in policy and in economic decisions. Conservation funding based on biased data can direct resources away from areas of greater need in a community. Recognizing current biases by way of data and data interpretation is the first step towards creating a more inclusive understanding of environmental needs.

Digital tools shape who tells the story of climate change. Without equitable access, marginalized voices—especially youth, Indigenous leaders, and communities of color—are excluded from the public dialogue.
Digital equity supports climate justice storytelling, advocacy, and democratic participation.

Without equitable digital access, communities cannot fully participate in, benefit from, or shape climate solutions that affect their future. Youth who engage as contributors of data rather than consumers get to contribute to a theory of change. Engagement in community-based science can be meaningful and transformational when themes and trends are applied and understood in their own communities.

Community-based data promotes empowerment and ownership. When communities are actively involved in data collection they are empowered to use it to advocate for themselves and in driving needed change.

There has perhaps never been a even greater need for the involvement of local and community-based science. We need to ask, who gets to contribute to science and what data counts and what doesn't? Who gets to decide? How might systems of power impact how cultures interact and engage with their environment over time?

The Need for CITL (Community in the Loop) in partnership with AI data

AI becomes more powerful and equitable when CITL (communities in the loop) are integrated with the use of AI generated data.

We believe that the strongest AI systems will contain the following elements:

-Satellite data + community sensors

-AI models +human interpretation

-Big data + local stories

Satellite data can tell us what changed, where it changed, overall patterns, and themes.

Community-based data (CBD) tells us how change impacts humans and our planet.

It can tell us who is impacted whether socially, culturally or economically.

It is based on real-time observations and the lived experience.

Both AI and CBD are needed. CBD tells the story of what AI can't see.