Ghosts in the AI Machine: Unforgetting the Artificial Histories Haunting Educational Futures

Session Type: Symposium

Abstract

Scholars across disciplines ask: What does it mean for educators to consider AI as an agent of change if we do not consciously calculate the weight of AI’s histories on the change we hope to make? The papers expose the psychosocial harms of stereotype threat on already vulnerable learners from AI; offer empirical evidence that AI discriminates by socio-economic status when grading computational assignments with clear correct and incorrect answers; model pedagogical practices to explore the ecological impacts of AI; and provide an ethical and justice centered framework for adopting AI into schools. Taken in sum, the symposium analyzes AI technologies acting as accidentally harmful technological agents of change, offering purposeful responses for teaching toward more just technological futures.

Sub Unit

• SIG-Technology as an Agent of Change in Teaching and Learning

Chairs

• Marie K. Heath, Loyola University Maryland

• Stephanie Smith Budhai, University of Delaware

Papers

• Data is Power: Positioning K-12 Youth as Participatory, Sociotechnical AI Justice Researchers - Evan Shieh, Young Data Scientists League; Thema Monroe-White, George Mason University

• Dollar General or Whole Foods? Math Grading Bias as a Warning Sign for AI in Education - Melissa Warr, New Mexico State University

• Designing a Guide for Equitable AI and Tech Justice in K-12 Education - Shana Vidal White, Kapor Foundation; Marie K. Heath, Loyola University Maryland

• From Corporate Greenwashing to Thirsty Data Centers: How Young People Analyze GenAI’s Environmental Costs - Charles Logan, Northwestern University

Session Organizer

• Marie K. Heath, Loyola University Maryland

Discussant

• Daniel G. Krutka, University of North Texas