“They created segregation with the economy”: Using AI for a student-driven inquiry into redlining in the social studies classroom

ABSTRACT

This article investigates students’ engagement with a historical inquiry into redlining—a practice of discriminatory lending that originated in the 1930s as part of the New Deal. The authors developed and implemented a week-long curricular intervention for high school sophomores using StoryQ—an Artificial Intelligence (AI) textual modeling platform designed for high school students without technical expertise—to examine hundreds of neighborhood descriptions produced for the Home Owners Loan Corporation’s “residential security maps” in the late 1930s. In this article, we ask: What kinds of historical and present-day racial awareness do high school students demonstrate through instruction focused on AI-assisted analysis of patterns in redlining? Analyzing field notes, interviews, and student-generated digital work showed that many students were drawn to structural explanations of racism and worked to unpack the way primary sources presented Whiteness through “coded language.” We argue that it is not only possible for teachers to construct historical inquiries that aim to identify patterns in a large set of primary sources with the aid of AI, but this approach to inquiry offers students an important avenue to engage with alternatives to individual conceptions of racial oppression.

KEYWORDS:

• Artificial intelligence
• historical inquiry
• primary source
• racial literacy
• redlining

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. All names are pseudonyms.

Additional information

Funding

This work was supported by the Data Science Academy (NCSU); Data Science Academy (North Carolina State University).