Scaling Proven Learning Practices

Fall is in the air, school is back in session, and we're feeling a lot of momentum behind our work to scale proven learning practices to benefit every learner.

In that spirit, I'm excited to share a few announcements. Today, we're making an early release of our Knowledge Graph, which makes AI-generated content more accurate, available to the education field. We're also integrating Knowledge Graph with Claude, Anthropic's large language model. And we're providing early access to tools called Evaluators, which help ensure AI-generated text is accurate, educationally rigorous, consistent with academic standards, and worthy of teachers' trust.

We think of these releases as building blocks for the entire education community - open, public infrastructure to support AI tools that reflect how students learn, make the best of learning science accessible, and embody our highest aspirations for learners.

The Challenge and the Opportunity

We've been working in education technology for a decade. There are some amazing tools available to teachers and students - but if you look at the broader ecosystem, the story is more complicated. If you're an educator, you're probably spending more time than you'd like stitching curricula and tools together. The process is technical and laborious, and too often, it crowds out more meaningful work. Edtech is supposed to make it easier to give students a cohesive learning experience that caters to their individual needs. But right now, the reality is falling short of the promise.

Clearly, AI tools could compound these challenges - becoming just another piece of technology to manage. But we believe there's a future where the next generation of edtech tools supports and empowers teachers in ways they never have before. With the right data, AI tools can integrate everything we know about the science of how kids learn, plus state academic standards and a world of high-quality learning materials. This will help educators do what they do best: instruct, engage, and support their students.

A technology transition also gives us the opportunity to re-engineer the ecosystem itself. We think it's possible to build edtech tools that share a common language for learning. Tools like that will be easier for teachers to use and connect to their practice. They'll also help students develop competencies that stretch across different subject areas, and build up the skills they'll need to flourish within and beyond school.

The key to realizing that vision is laying the right foundation. AI systems are only as good as their inputs. What's needed are machine-readable datasets and evaluation methods that will give AI systems the education context they need to deliver great results. Today, these kinds of resources are available to just a small fraction of edtech developers; they must be open and accessible to all education stakeholders.

These are the goals we're pursuing - not on our own, but in close partnership with educators, researchers, and other experts. The building blocks we're releasing today are an important step forward. We're excited to see how they are used to scale proven teaching and learning practices to students everywhere.

A Navigation System for Learning

Knowledge Graph is one of the resources we're launching today. It's been in private beta for nearly a year, and today, we're publishing four machine-readable datasets on GitHub that developers can directly integrate into their tools.

This early release includes academic standards from all 50 states in four core subjects - English, math, science, and social studies. Another dataset breaks the math standards into smaller skills and concepts, which we call learning components. Other datasets in Knowledge Graph connect the components and standards to one another, so AI systems can understand education as a progression of ideas with certain pathways and prerequisites.

We're assigning a latitude and a longitude to every skill students need to master, then drawing routes between each skill and the rest. In this way, Knowledge Graph is a bit like the data layer that sits underneath products like Google Maps and Apple Maps. This data can be used to create tools to help teachers and students get where they need to go - what we might one day think of as a GPS for different paths to learning.

Bringing Claude to the Classroom

That brings me to the next announcement, which is that we've built a custom model context protocol (MCP) server that connects Knowledge Graph to Anthropic's LLM, Claude.

A lot of educators already use Claude to help them develop lesson plans and problem sets. With the integration, Claude will have the context to give responses that reflect state academic standards, learning progressions, and the learning science research that's embedded into Knowledge Graph.

We're excited for teachers to try it out. And if you're a developer, we hope the integration is an inspiring example of what you can do with Knowledge Graph. We designed our MCP server to work with any AI system that supports the protocol. We look forward to expanding access to more users in the near future to ensure high-quality educational materials reach more educators and learners.

Evaluating AI tools so teachers can trust them

Finally, I want to turn toward one of the most common applications for AI in education, which is generating practice exercises for students.

Clearly, there's a lot of potential to tailor material for students' strengths and interests - and a lot of potential for AI output to miss the mark. Getting systems to generate material that's always accurate and rigorous is one of the hardest problems in edtech, which is what led us to build tools called Evaluators. These tools measure the quality of AI outputs against educational rubrics.

Our first Evaluators are focused on literacy for students in 3rd and 4th grades. They've anchored on a dataset we built in partnership with literacy experts at Student Achievement Partners - authors of the gold-standard SCASS rubric - and Achievement Network. These Evaluators review text and measure the complexity of the vocabulary and sentence structure to ensure tools provide students with reading passages that can further their learning. We've also built an Evaluator to assess the grade-level appropriateness of AI-generated material through the lens of text complexity.

Today, we're launching an early release of the prompts, logic, and scoring code for these Evaluators under open licenses. In the months ahead, we'll enhance and expand them to cover other measures of text complexity and more grade levels. We'll also release Evaluators to assess AI-generated output against other rubrics, from alignment with state academic standards to how motivating an exercise is for students.

Our Next Chapter

Over the past decade, we've learned a lot about the challenges and opportunities in our education system. We've said since the beginning that technology isn't a silver bullet for any of them. But the right tools really do have the potential to transform teachers' and students' lives for the better - and that's never been truer than it is today.

We're committed to building the core AI infrastructure to support educators in the classroom. As we deepen our partnerships with educators, researchers, and developers and prepare to move our tools from private beta to general availability in 2026, the Chan Zuckerberg Initiative's work in education will now be called Learning Commons - a name that reflects our sharpened focus and role within the larger education ecosystem.

While our name changes, our values remain the same. We will keep working for a future where education and technology unlock student potential and accelerate meaningful progress for all. And to do that, we will continue collaborating across the education ecosystem - co-building the future and the technologies we believe in with teachers, school district leaders, researchers, and developers.

We begin this next chapter with boundless optimism for this movement and for the future of education.

We also hope you'll join us in this work. You can inquire about our new products and partnership opportunities, and follow along with Learning Common's work at learningcommons.org.