Fellowships

by Weiying Li. What does genuine partnership look like when building AI for education? Working with middle school teachers and computer scientists, we co-designed AI dialogs where teachers are valuable contributors to refine what the AI understands as valuable thinking. Through iterative refinement, teachers identified precursor ideas and observations that predicted future learning, and refined guidance design in the dialog. Our AI dialog sees learning the way teachers do, built through genuine collaboration where both model development, learning sciences theories, and teachers' classroom expertise work together from the start, not just at the end.

by Jose Aguilar. I reflect on my uneasy relationship with being called a data scientist. Despite training in computer science and utilizing computational tools in education policy, I struggle with how data science often strips away human narratives and reinforces existing inequities. My identity as a first-generation, queer, Latinx scholar deepens these tensions, prompting me to explore frameworks such as QuantCrit and critical data science. Ultimately, I utilize research that bridges computation and critique, advocating for more human-centered, politically aware approaches to data that integrate lived experiences alongside data findings.