A Note from the Author about Learning Machines
This book began as a graduate thesis at Johns Hopkins University. But the more I wrote, the more I realized that policy advocacy wasn't enough. What I was really uncovering was something older and stranger: the way historical debates about human nature — what people are for, what they're capable of, who gets to decide — have been quietly absorbed into the algorithms now shaping millions of students' lives. Learning Machines is my attempt to make that visible.
About the Book
When a school district deploys an AI tutoring system, it isn't just choosing a tool. It's making a claim about what learning is, what students are capable of, and whose vision of human potential gets encoded into the future.
Learning Machines traces how centuries of competing educational philosophy — from Luther's literacy agenda to Jefferson's selective meritocracy to Friedrich List's nationalist economics — now live inside the machine learning systems reshaping global education. Drawing on policy research, data analysis, and critical theory, Aminata Daramy asks: whose assumptions are we automating? And who gets left out?