Bringing Systemic Racism Into View: Using Artificial Intelligence Modeling for a Student-Driven Inquiry Into Redlining in the Social Studies Classroom

• In Event: Inquiry With Youth in Pursuit of Truth

Abstract

This paper presents findings of a historical inquiry into redlining built on a unique pairing of educational technology and primary source investigation. We developed and implemented a week-long curricular intervention in which high school sophomores used an AI textual modeling platform to examine hundreds of neighborhood descriptions produced for the HOLC’s “residential security maps” in the late 1930s. In this paper we ask: How did student participation in modeling text from redlining sources shape their historical and present-day racial awareness? Drawing on field notes, interviews, and surveys, we highlight two key themes about the way students reasoned historically: 1) students were drawn to structural explanations of racism; 2) students unpacked the way primary sources presented Whiteness through “coded language.”

Authors

• Amato Nocera, North Carolina State University
  Presenting Author

• Victoria Newton, North Carolina State University
  Presenting Author

• Shiyan Jiang, North Carolina State University
  Presenting Author

• Jennifer K. Houchins, WestEd
  Presenting Author