Edtech Equity

by Nidhi Hebbar and Madison Jacobs, with support from Matthew Volk and Elizabeth Allendorf

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Black and Brown students in American public schools battle discrimination in many forms: higher rates of suspension, less advanced academic tracks, and a lack of cultural responsiveness from predominantly white teachers. Edtech products that use AI and machine learning can amplify already existing biases and introduce new ones. Education technologies promise to personalize learning, identify at-risk students, and automate administrative tasks for educators, but many do not take into account the unique challenges facing the Black and Brown students that make up over half of the American K-12 public school population. This project proposes a Racial Equity Toolkit for AI in Edtech to enable companies to uncover and mitigate racial bias at each stage of their product design and development, from ideation to implementation. Additionally, it puts forward procurement guidelines to help schools assess edtech platforms for racial equity before purchasing.

Click below to view various resources for navigating Edtech Equity, including a website full of resources, a robust design toolkit for edtech developers, and a procurement guide for schools to use when assessing, or working with, edtech companies.