Our Approach
We believe better educational AI will emerge from discovering what classrooms actually need, building solutions with educators, testing them in real classrooms, and using what we learn to improve the technology. That's why every AugmentED project combines research and development in an iterative co-design cycle, carried out by teams of educators, researchers, and engineers.
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 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.
01 Define the role
We start by asking what teachers and students need.
Each cycle focuses on one role AI could play in meeting real classroom needs, such as deepening a teacher's understanding of her students' prior experiences and interests, assessing complex skills, or facilitating feedback. We select and shape each role with researchers, educators, and engineers. That role becomes the North Star for everything that follows.
02 Build the capabilities
We research what makes that role technically feasible.
Our researchers and engineers then build the underlying infrastructure, with educators informing the work: 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.
03 Co-design the tools
We build tools that bring the role to life.
Our teams of educators, researchers, and engineers 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.
04 Test, learn, begin again.
We test in real classrooms.
Every tool is tested in classrooms by teachers and students. What we learn tells us which capabilities to build or improve next, while new and improved capabilities make better tools possible. Then the cycle turns again.
Each turn 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.
Through this cycle, we produce three things the field needs:
Research
Every co-design cycle generates evidence about what AI should do in education, when it works, and why. We openly share our research findings, evaluation methods, and models for teaching and learning alongside AI, so the field can build from evidence instead of assumptions.
Capabilities
Behind every successful classroom tool are foundational capabilities that make it possible. We develop and share reusable capabilities, from specially trained models and datasets to ways of representing what's being taught and learned in a classroom, that any educational AI application can build on.
Applications
Research and capabilities only matter if they improve learning. We build and validate classroom tools and teaching methods alongside educators and students to demonstrate what works in practice.