AI and Educational Justice: A Critical Conversation

Session Type: Invited Speaker Session

Abstract

Why does education need artificial intelligence (AI), and what does AI mean for educational justice? While optimism surrounds the growing use of AI in education, we challenge this uncritical embrace. As yet, education researchers have not fully examined the potential and documented harms AI poses—particularly to minoritized students, their schools, and communities. This presidential session aims to open a research-based dialogue on the risks and potential benefits of AI in education, given what our limited evidence tells us thus far. Framed through a lens of rigorous skepticism, we question whether AI is truly necessary for advancing education or educational justice. We will explore both the well-documented harms from interdisciplinary research and engage in thoughtful speculation about future risks.

Sub Unit

• AERA Presidential Session

Chairs

• Niral Shah, University of Washington

• David Stroupe, University of Utah

Papers

• Team 1 - Sarah Schneider Kavanagh, University of Pennsylvania; Tess Bernhard, University of Pennsylvania

• Team 2 - Christina Krist, Stanford University; Elizabeth B. Dyer, University of Illinois at Urbana-Champaign; Eugene M. Cox, University of Illinois at Urbana-Champaign; Joshua M. Rosenberg, University of Tennessee

• Team 3 - Sepehr Vakil, Northwestern University; Charles Logan, Northwestern University; M. Danny Caballero, Michigan State University

• Team 4 - Aman Yadav, Michigan State University; Anne Drew Hu, Michigan State University; Rosa Angela Calosso, Graduate Center - CUNY; Daniel Reinholz, San Diego State University

• Team 5 - Tingting Li, Washington State University