Fellowships

Beyond the Hype: How We Built AI Tools That Actually Support Learning

November 12, 2025
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.

NEW: Google Latinx AI Fellowship

Program Components Fellows will participate in a program that includes: Skills Development. LXAI Fellows will complete a structured introduction to D-Lab workshops, including some custom workshops developed specifically for this cohort. Community Building. LXAI Fellows will be invited to join the D-Lab community and have the opportunity to attend research talks by D-Lab members, fostering intellectual exchange and interdisciplinary collaboration. LXAI Fellows will participate in community-building events both across D-Lab and within their cohort...

Forecasting Social Outcomes with Deep Neural Networks

October 7, 2025
by Paige Park. Our capacity to accurately predict social outcomes is increasing. Deep neural networks and artificial intelligence are crucial technologies pushing this progress along. As these tools reshape how social prediction is done, social scientists should feel comfortable engaging with them and meaningfully contributing to the conversation. But many social scientists are still unfamiliar with and sometimes even skeptical of deep learning. This tutorial is designed to help close that knowledge gap. We’ll walk step-by-step through training a simple neural network for a social prediction task: forecasting population-level mortality rates.

Why I Don’t Call Myself a Data Scientist: A Researcher's Journey

October 1, 2025
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.

D-Lab Data Science & AI Fellowship

Overview

Data Science & AI (DSAI) Fellows are an integral part of the D-Lab community. They largely support the D-Lab’s instruction program, while additionally bolstering the D-Lab’s interdisciplinary research mission by delivering talks and writing blog posts.

The DSAI Fellowship is intended for Berkeley students who wish to provide sizable and wide-ranging contributions to the D-Lab community. Those interested in the fellowship are expected to have technical competency in data-driven tasks (whether qualitative or quantitative), and a...