Our Approach
We believe better educational AI will emerge from discovering what classrooms actually need, building solutions with real educators and students, and testing them in real classrooms. What we learn from that testing flows back into better capabilities and tools. That’s why every AugmentED project combines research and development in a continuous co-design cycle.
Education technology companies build the applications teachers and students use every day. The large AI labs build the underlying models. What's missing is the layer in between—the translational capabilities that let AI understand what a student knows, measure skills as complex as critical thinking, and adapt to what's happening in a real classroom. That's the layer we are building.
We begin by asking what role classrooms need AI to play.
We begin by asking, not what AI can do, but what teachers and students need. At the start of each co-design cycle, our teacher and research partners define a role AI can play to meet a real classroom need, such as enhancing a teacher’s understanding of her students’ prior experiences and interests, assessing complex skills, or facilitating feedback. That role becomes the north star for everything that follows.
We research what makes that role technically feasible.
Our educators, researchers, and engineers then build the underlying infrastructure: the reusable technical capabilities a tool needs to play its chosen role well. For the roles we’re currently exploring, that includes building capabilities such as validated ways for AI to measure and support durable skills and a living map of how the ideas in a particular teacher’s class connect to each other and to students’ prior experiences and interests. Most of these capabilities don’t exist yet. Once we build and prove them, they can be reused, adapted, and made available to others to build on.
We build tools that bring the role to life.
Our interdisciplinary teams—teachers, researchers, engineers, and designers working as true partners—use that infrastructure to build and test AI-powered tools, and new ways of teaching alongside them. This second part is crucial: an AI tool might help students evaluate sources or collaboratively solve problems, but it’s only truly effective when paired with a teaching approach that combines what AI and human teachers each do best.
What we learn shapes what we build next.
Every application is tested in real classrooms alongside educators and students. What we learn tells us which capabilities to build or improve next, while new and improved capabilities make better applications possible. Together, they form a continuous research and development cycle.
Each iteration of the cycle strengthens the field’s understanding of what roles AI should play, what capabilities those roles require, and how those ideas translate into practical tools that genuinely improve learning.