Allison Atteberry

Allison Atteberry is an Associate Professor of Education and Public Policy at the University of Virginia and Director of EdPolicyWorks, a joint research center of UVA's School of Education and Human Development and the Frank Batten School of Leadership and Public Policy. An applied education policy researcher, she uses quantitative methods to study how educational inequality is produced and whether the policies we invest in can improve students’ outcomes. She conducts this work in partnership with public education agencies and aims to make it genuinely useful to those making policy decisions, while meeting high standards of rigor.

Her research agenda has centered on three "pivot points" with the power to shift student trajectories: (1) the teachers students are assigned to during the school year, (2) their access to quality early childhood education before school begins, and (3) their unequal experiences over the summers in between. (4) A fourth, emerging strand explores how the sudden availability of generative AI tools is reshaping teaching, learning, and human development for students and educators.

1. Supporting Access to Effective Teachers. Much of her work asks a question policymakers face directly: how do we get strong teachers into the classrooms that need them most, and keep them there? Her work has shown that early-career performance strongly predicts later effectiveness, documented the destabilizing "churn" of teachers within and across schools and its costs for historically underserved students, and evaluated compensation and evaluation policy — including an analysis of Denver's ProComp, one of the nation's earliest pay-for-performance systems.
2. Early Childhood Education Policy. In early childhood, she has led a multi-year randomized trial of full-day versus half-day prekindergarten in a predominantly Hispanic, low-income Colorado district. She has evaluated whether retention-bonus offers in Maryland reduce turnover among credentialed early-childhood educators — with the largest effects concentrated among early-career and lower-wage staff.
3. Schools, Summers, and the Growth of Inequality. In her work on out-of-school time, she has used large longitudinal datasets to show how much of the achievement gap grows during the summers rather than the school year, and, in a reconsideration of the classic Coleman Report, that schools may shape how fast students learn even more than their mean achievement levels.
4. Influence of Generative AI on Teaching and Learning. A fourth, emerging strand asks more open-ended questions about the sudden ubiquity of generative AI: Will these tools make us stronger learners and thinkers or quietly erode the skills that have historically come only through persistence and struggle? Which capabilities must humans still master versus which can responsibly be offloaded to machines? How should we re-imagine what gets taught in classrooms? Atteberry has begun exploring these questions in her new course, Generation AI: Innovation, Skepticism, and the Future of Education, in which students study their own AI use — for instance, comparing whether those who lean on AI to summarize a reading engage with it as deeply as those who read it themselves. A related line explores whether large language models might help surface patterns in teacher-student interaction — including ones human observers tend to miss — across large sets of classroom transcripts.

Atteberry’s research has been generously supported by the Institute of Education Sciences, Arnold Ventures, the William T. Grant Foundation, Smith Richardson Foundation, and the Russell Sage Foundation, among others.

Ph.D., Stanford University, 2011
B.A., University of Chicago, 2005

Curriculum Vitae

• Teacher- and school-level interventions designed to improve the quality of instruction experienced by historically underserved students
• Policies and interventions that are intended to help provide effective teachers to the students who need them most
• Areas of specialization include teacher labor markets, education policy analysis, summer learning loss, production of social inequality, quantitative method and causal inference