AugmentED is a team of educators, researchers, and technologists working together to build the evidence base for what AI should (and shouldn’t) do in the classroom, and the technology to do it well.
The Challenge
Ready or not, AI tools have arrived in the classroom. Many companies and schools are rushing in before we know what AI can do well, what teachers uniquely bring, or what would actually benefit students. Today’s tools rely on general-purpose AI that was never designed for education, and it shows. We need better evidence on the roles AI should (and shouldn’t) play in schools, and purpose-built technology that can bridge between AI and real classroom needs.
Our Approach
We run an iterative research and development cycle, with educators, researchers, and engineers as equal partners at every step. We start by asking what teachers and students need. Each cycle focuses on one role AI could play in meeting some of those needs. Together, we identify what the role requires but doesn't yet exist, build it, and test it in real classrooms. What we learn informs the next turn of the cycle.
Each turn of the cycle informs the next.
Tap a step to read more. Each turn of the cycle informs the next.
Each co-design cycle produces more than prototypes. It creates evidence about what students need, what AI can do reliably, and what teachers need to use it well. AugmentED turns those lessons into research, technical foundations, and tools the field can trust and build on.
Ideas and evidence that help the field reason about AI in education: research insights on the roles AI should (and shouldn’t) play in the classroom and methods for evaluating how well AI can play those roles.
Reusable infrastructure that future classroom tools can be built on, from validated AI capabilities to the datasets that power them. We build these to work across many subjects and types of schools.
Tested classroom resources that help educators apply AI, including AI-powered applications, implementation guides, and proven methods for teaching alongside AI, all built with teachers, not for them, to solve real challenges in their classrooms.
Our Current Work
In our first cohort, teachers from several pioneering high schools worked with researchers and engineers to co-design AI-powered tools. Each project below is a work in progress, and each one feeds reusable capabilities back into a foundation that others can build on.
All three put the same AI role to work: AI as a “cognitive extender.” In this role, AI surfaces patterns hidden in more information than any individual teacher or student could track alone.
Crosstown High
Students learn more deeply when new material connects to what they already know and care about. This tool maps a teacher’s course material to each student’s pre-existing knowledge, experiences, and interests and then suggests personalized connections.
Museum High School
During multi-week projects, students respond to teacher prompts with short voice memos. The tool surfaces where each student and group is progressing, getting stuck, or drifting off track, so teachers can provide specific, meaningful feedback for every student.
High Tech High Mesa & High Tech High International
After students reflect on their small-group work, the tool generates insights about how each group is functioning. It makes group dynamics visible to teachers in real time, so they can coach students on collaboration and repair interpersonal problems before they harden.
Our Thinking
We work with university and research partners to study what each classroom role for AI makes possible, where it falls short, and what we can build to shift those limits. We publish what we find so the whole field can move forward together. Here are three recent papers:
Arguing over whether AI is good or bad for education misses the point: AI isn't one thing. We define five distinct roles AI can play in the classroom, and show how naming a tool's role determines what it should do, how to judge whether it works, and what safeguards it needs.
Can AI help assess a skill as complex as critical thinking? We show that AI models—when given skill definitions and training—can measure some critical-thinking subskills in student writing, and we map where they still fall short.
We share a standardized, step-by-step process for improving and validating the capacity of AI systems to detect complex skills like critical thinking, so the field can build AI measurement tools without starting from scratch every time.
We co-design with educators, publish what we learn, and create infrastructure the whole field can build on. If that's the future of AI in education you want, come build it with us.
