Engaging K-12 Learners in Data Annotation for AI Climate Models

Due to the climate crisis, summers in Greenland have been rapidly getting warmer, causing increasing rates of ice melt on the Greenland ice sheet and speeding up sea-level rise. Evidence of this change can be measured by the number and location (elevation) of water pools and lakes that form on the surface of the ice sheet. In addition, crevasses can cause lakes to drain extremely rapidly causing the ice to flow faster, contributing to sea-level rise. However, the lack of annotated data makes it difficult to automatically detect and track these behavioral changes in the polar ice sheet lakes. This study describes how a team of polar and data scientists actively engaged middle and high school students in their classrooms in a data annotation process through an engaging curriculum unit to identify multiple ice sheet phenomena observed in satellite imagery. The findings describe the learning outcomes from both student and teacher perspectives. It also projects learners’ understanding and sentiments about climate change and the role of artificial intelligence (AI) models coupled as an extension of citizen science in addressing climate change.