Sessions at ASU+GSV

Raising the Bar: Building Quality, Trust, and Learning Science into AI Education Tools
Tuesday, April 14, 10:10–10:50 AM PT — Harbor E
Sandra Liu Huang, President of Learning Commons, will join a panel conversation on how thoughtful design, transparency, and alignment with learning science can distinguish genuinely transformative tools from those that merely automate existing practices or add to classroom clutter. The session will challenge the edtech sector to prioritize pedagogical integrity and user agency over rapid deployment, setting a higher standard for what belongs in educators’ and students’ essential toolkit.

The Human-AI Partnership in Education Data: Why Discernment Makes Intelligence Actionable
Tuesday, April 14, 3:00–3:50 PM PT — America’s Cup C&D
Tyler Sussman, Director, Philanthropic Strategy and Partnerships at Learning Commons, will join other leaders working at the intersection of education and data to examine how AI, generative AI, and machine learning expand analytical capacity and accelerate insight—while deploying human discernment to ask the right questions, interpret outputs, and turn intelligence into action. This session will explore how leaders working across high-quality materials and assessments are viewing both supply and demand actors integrating human and AI efforts to build, implement, and measure the quality of the strongest tools for students.

Is AI Eliminating Productive Struggle — and With It… Learning?
Tuesday, April 14, 3:50–4:30 PM PT — K–12 Track
Vice President of Product Kristin Vincent will moderate a discussion exploring how “productive struggle” shows up in an AI-assisted learning environment, and where the line lies between meaningful support and cognitive shortcutting. The conversation examines what rigor really means when students have access to instant feedback, hints, and solutions. Bringing together perspectives from learning science, instructional design, classroom practice, and education technology, this session offers a grounded look at how AI can be used intentionally: not to replace thinking, but to deepen it. 

What is Creativity and Learning in the Age of AI?
Wednesday, April 15, 10:10–10:50 AM PT — Harbor H, Level 2
Dan Quine, Senior Director of Engineering and AI at Learning Commons, will moderate a panel examining what happens to mastery when output is abundant — and how professional creators evolve in a world of infinite generation. AI can now draft marketing copy, design graphics, generate video avatars, remix audio, and simulate entire creative workflows. But when creation becomes frictionless, what changes? This session explores how AI is reshaping authorship and ownership, whether prompting is the new creative skill, and the rise of direction, taste, and curation as core competencies. 

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Launched in 2025, Learning Commons builds on the Chan Zuckerberg Initiative’s decade of work advancing learning science and translating research into classroom practice. Through shared, open technological infrastructure built for the public good, Learning Commons aims to better connect the way students learn with the tools they use.

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These partnerships will help further the development of Knowledge Graph, which integrates trusted instructional content, academic standards, and research directly into AI-powered tools — improving precision, relevance, and instructional alignment.

“Since we announced the early release of Knowledge Graph last fall, it is becoming an essential data layer for the edtech field,” said Sandra Liu Huang, president of Learning Commons. “We are proud to partner with Magpie, ANet, and New Meridian to continue the development of open, public infrastructure to strengthen the next generation of AI‑powered instructional tools for the benefit of teachers and learners.”

The grants announced today will expand and validate Knowledge Graph’s math learning components, as well as support the development of a map of literacy skills grounded in the science of reading. 

Over the past two decades, 44 states and Washington, D.C., have passed laws to strengthen literacy instruction. Yet translating policy into classroom practice requires research-based tools and ongoing support for teachers. To improve the tools available to educators, Magpie Literacy will expand its foundational map of literacy skills and make it open-source, allowing educators, researchers, and developers to build reading tools with a common, research-aligned framework for organizing literacy knowledge. 

“I am certain that we can eradicate illiteracy in America in our lifetimes. Two key steps toward this goal include aligning research to practice and supporting individual learner needs at all grade levels,” said Rebecca Kockler, founder and CEO of Magpie Literacy. “Making our foundational map of literacy skills widely available will enable more tools grounded in learning science to support more students.”

The two other projects will further refine Knowledge Graph’s math learning components developed by ANet. Learning components are skills and concepts that map to academic standards but are more discrete and precise than academic standards alone. ANet will revise and expand the math learning components dataset, and pilot the updated components with school districts through Compass, ANet’s assessment platform, ensuring the work is grounded in real classroom practice and teacher decision-making.

“Curriculum, assessment, and instruction are too often designed and implemented as separate systems, which makes it hard for teachers to use assessment results to guide instruction,” said Michelle Odemwingie, CEO of ANet. “By strengthening the math learning components in Knowledge Graph, we’re helping create a shared foundation that allows AI-powered tools to better support instructional planning and next steps for learning.”

Finally, New Meridian will deepen and quantitatively validate the math learning components developed by ANet. This research will determine the difficulty level of each learning component and uncover optimal learning trajectories through those components to guide curriculum design and instruction. 

“Assessments are essential for measuring student achievement, but they should also drive meaningful learning experiences in the classroom,” said Arthur VanderVeen, CEO of New Meridian. “We are excited to partner with Learning Commons to develop resources that improve the alignment between assessments and instruction to ensure AI-powered tools can be trusted by teachers.”

All of the resources created through these investments will be openly available, reflecting our commitment to building public infrastructure for AI in education. These grants build on the early release of Knowledge Graph and Evaluators and our work to translate learning science into the tools, data, and frameworks the field needs to build responsibly. 

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Launched in 2025, Learning Commons builds on the Chan Zuckerberg Initiative’s decade of work advancing learning science and translating research into classroom practice. Through shared, open technological infrastructure built for the public good, Learning Commons aims to better connect the way students learn with the tools they use.

The post Learning Commons expands partnerships to support high-quality instruction appeared first on Learning Commons.

Learning Commons announced $2.8 million in grants to improve AI tools that support educators in providing high-quality literacy and writing instruction. These projects will advance the development of Evaluators that assess the educational quality of AI-generated content and feedback against high-quality datasets and trusted educational rubrics.

“Teachers deserve trustworthy classroom tools that provide high-quality, rigorous content. Tools should deliver content at the right grade level, tailored to each student’s needs, and based on solid learning science to help students grow,” said Sandra Liu Huang, president of Learning Commons. “We’re proud to work with Student Achievement Partners, Quill, and Leanlab Education to create public tools that assess how well AI tools meet these important measures.” 

The three grants announced today address enduring classroom challenges: how to share timely and consistent feedback on student writing and how to provide students with literacy content tailored to their development. Many AI-powered edtech tools claim to offer solutions, but they vary in effectiveness, often providing repetitive, generic outputs that aren’t sufficiently challenging and actionable for teachers and students.

“Young writers need lots of practice and specific feedback, which is time-intensive for teachers,” said Peter Gault, executive director and co-founder of Quill, one of the three grantees. “AI-powered tools can support teachers in providing more frequent and detailed feedback, but only if those tools are rigorously evaluated against high-quality standards.”

To raise the quality of feedback that AI tools can offer — and ultimately make them suitable to help teachers give students more frequent and detailed guidance — Quill and Leanlab Education will develop a research protocol and a large, public dataset. The dataset will include teacher feedback on anonymized samples of informative student writing, annotated to highlight effective feedback practices by researchers with expertise in learning science. Developers will then be able to use this dataset to evaluate the quality of current AI tools’ feedback on student writing and train future AI tools to provide high-quality feedback — aligned with research-backed strategies to strengthen student writing. 

“Our proximity to schools, students, and educators, paired with a rigorous R&D approach, allows us to ensure that tools of the future are being designed in partnership with school communities. We believe this level of direct engagement with students and educators is a necessary industry standard to build trustworthy and effective AI tools,” said Katie Boody Adorno, founder and CEO of Leanlab Education. 

The third grant will support the continued development of text complexity Evaluators. Earlier this year, Learning Commons launched Evaluators focused on literary text for students in third and fourth grades. Developed in collaboration with Student Achievement Partners, the models can read AI-generated text and measure the complexity of the vocabulary and sentence structure. Student Achievement Partners will use this new grant to extend beyond vocabulary and sentence structure to develop a machine-scorable version of the full qualitative text complexity (SCASS) rubrics and use those rubrics to score a dataset of passages for evaluating literary and informational text complexity across all dimensions of their SCASS rubric for grades 3-12.

“To build strong readers in grades 3-12, students need consistent access to texts that are worth reading. Texts that support grade-level comprehension, build knowledge, and invite productive struggle without confusion,” said Joy Delizo-Osborne, president and CEO of Student Achievement Partners. “This work turns the full qualitative text complexity rubric into a transparent, machine-scorable yardstick, so AI tools can be evaluated against research-backed expectations and teachers can trust that the passages and recommendations they receive will actually strengthen comprehension.” 

All of the resources created through these investments will be openly available, reflecting our commitment to building public infrastructure for AI in education. These grants build on the early release of Knowledge Graph and Evaluators and our work to translate learning science into the tools, data, and frameworks the field needs to build responsibly. 

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Launched in 2025, Learning Commons builds on the Chan Zuckerberg Initiative’s decade of work advancing learning science and translating research into classroom practice. Through shared, open technological infrastructure built for the public good, Learning Commons aims to better connect the way students learn with the tools they use.

The post Learning Commons announces new commitments to advance student literacy and writing appeared first on Learning Commons.

The response to the early release of Knowledge Graph and Evaluators exceeded our expectations. On GitHub, there have been thousands of downloads of the Knowledge Graph datasets and Evaluators resources. We have been blown away by the number of interesting use cases across edtech partners who can now bring higher-quality resources to educators by integrating our tools.

It’s clear that there’s a real need for open public infrastructure to support AI tools that are built and tuned for the classroom. With a shared foundation of quality and rigor, we believe that these tools can live up to their promise and help educators better support each student’s learning journey.

Our initial release was just the beginning. We continue to work in collaboration with teachers, district leaders, researchers, and developers to improve Knowledge Graph and Evaluators. You’ll see regular releases and updates to these resources as we continually improve them.

We are pleased to announce enhancements to Knowledge Graph, including the integration of math learning standards from Washington, D.C., as well as alignment between math learning components (smaller skills and concepts) and academic standards for Louisiana, Montana, Pennsylvania, and Washington, D.C. 

With this update, Knowledge Graph now includes alignment between math learning components and academic standards for 21 states and D.C., making it easier to adapt math content for use across the country. Later this month, we will add Wisconsin to the list and release other improvements. All of these resources will be available with an open license on the Knowledge Graph page on GitHub.

In the coming months, we will release additional updates to Knowledge Graph and expand Evaluators within literacy for 3rd and 4th grade to more dimensions of text complexity and cover additional grade levels. We will also release Evaluators that assess AI-generated output against other rubrics, such as alignment with state academic standards and how motivating the exercise is for students. 

It is an honor to work with a wide range of partners who share our vision of building public-good infrastructure to scale proven teaching and learning practices to benefit every learner. We hope you will join us. Stay connected and learn about new products and features on GitHub, follow us on LinkedIn, and reach out about partnership opportunities through our website.

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In that spirit, I’m excited to share a few announcements. Today, we’re making an early release of our Knowledge Graph, which improves the accuracy of content generated by AI-powered tools, available to edtech developers everywhere. We’re also integrating Knowledge Graph with Claude, Anthropic’s large language model. And we’re expanding early access to tools called Evaluators, which help ensure AI-generated text is accurate, rigorous, consistent, and worthy of teachers’ trust.

We think of these releases as building blocks for the entire edtech community — open public infrastructure to support AI tools that truly reflect how students learn, and that embody our highest aspirations for education.

The problem and the opportunity

We’re deeply optimistic about what’s possible. AI systems can give educators deep insights into every student’s learning journey — their individual challenges, strengths, and motivations — and make it possible to guide young people forward in ways that were never possible before, much less at scale.

We also know there are big challenges left to solve. Fundamentally, AI models are designed to help adults find answers quickly. The goal of education is to teach students how to come up with answers themselves. We think AI systems can play an important role in the process of learning, but they need to be carefully trained and tuned for that purpose.

As it stands, edtech developers are needing to do all the work themselves — which means wrestling with academic standards from all 50 states, summarizing decades of learning science research, and trying to distill that knowledge into their products.

For an individual developer, this work is expensive and time-consuming. For the wider edtech ecosystem, the result is fragmentation. Every tool is built on a different foundation and delivers a different level of quality. Teachers are struggling just to get a handle on the technology that’s out there, let alone using it to meaningfully enhance their practice.

But over the last couple of years, we’ve come to understand these challenges as a transformational opportunity, especially with AI as a tool for tool development itself.

CZI is a philanthropy with a world-class engineering team, deep knowledge of learning science, and strong connections to developers, researchers, and educators. We have a clear understanding of the infrastructure that needs to be built, and a longstanding commitment to making technology accessible and open source.

With that in mind, we’re working to create common, high-quality resources that AI systems can easily read and train on, and a shared and open foundation of quality and rigor for the next generation of education technology.

A navigation system for learning

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

Our 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.

In this way, Knowledge Graph is a bit like the data layer that sits underneath products like Google Maps and Apple Maps. We’re assigning a latitude and a longitude to every skill students need to master, then drawing routes between each skill and the rest. Developers can use that data 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’s responses will get a lot more specific — reflecting state academic standards, learning progressions, and learning science research that is 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, and we look forward to expanding access to more edtech developers in the near future.

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. Basically, they’re AI models that assess the output of other AI models.

Our first Evaluators are focused on literacy for students in 3rd and 4th grades. They’ve trained on a dataset we built in partnership with Student Achievement Partners — authors of the gold-standard SCASS rubric — and literacy experts at Achievement Network. The models can read AI-generated text and measure the complexity of the vocabulary and sentence structure. We’ve also built an evaluator that helps developers assess the appropriateness of a text for a particular K-12 grade band, based on its text complexity.

Today, we’re doing 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 the 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 infrastructure to support impactful AI tools in the classroom. As we deepen our partnerships with education technology developers and educators and prepare to move our tools from private beta to general availability of our tools 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.

Sandra Liu Huang
Head of Education and Vice President of Product
Chan Zuckerberg Initiative

The post Scaling proven learning practices appeared first on Learning Commons.

Today, the Chan Zuckerberg Initiative (CZI) announced the expansion of its work in artificial intelligence to help ensure education tools that leverage AI are grounded in research and best practices for teaching and learning.

CZI launched two new AI developer tools for education designed to empower developers to integrate high-quality education content seamlessly into their platforms. Knowledge Graph helps developers enhance AI system inputs by aligning them with learning science research, state academic standards, and curricula, while Evaluators help developers assess AI system outputs to ensure they meet the accuracy, rigor, and quality essential for teaching and learning.

Alongside these new private beta tools, CZI also announced the appointment of a new advisory board that includes experts in schools, data privacy, artificial intelligence, education technology, and learning science.

“With the rapid growth of artificial intelligence, improving the quality of outputs from large language models is increasingly important — especially where student learning and outcomes are involved,” said CZI’s head of education, Sandra Liu Huang. “CZI is partnering with education, research, and technology experts to help ensure artificial intelligence tools are high quality and support educator efforts to unlock the full potential of every student.”

These new tools are part of CZI’s efforts to help schools address everyday challenges by co-building tools with educators and empowering technology developers with resources to build high-quality, research-backed AI solutions for education.

Core AI resources for education developers

Through Knowledge Graph and Evaluators, CZI is using its learning science expertise and technical strengths to enable edtech developers to incorporate rigorous, high-quality educational content into their platforms and improve the overall infrastructure of AI-driven education products.

To help developers improve the quality of their inputs, Knowledge Graph will launch with two key interconnected datasets: a high-quality, openly licensed core math curriculum in partnership with Illustrative Math, and academic standards from all 50 states in partnership with 1EdTech.

For Evaluators, CZI also worked with academic experts to help edtech developers support teachers in closing the gap in student reading skills. They leveraged a Rubric for Literature from Student Achievement Partners to evaluate the complexity of AI-generated text outputs. They also worked with English Language Arts experts from The Achievement Network and Gradient Learning to assess the dataset.

The private beta phase includes initial collaboration with Playlab and Diffit, who are piloting the tools to improve their AI-based educational offerings.

Education Advisory Board

CZI is also announcing its Education Advisory Board, bringing together a diverse group of experts to help guide efforts in advancing the use of AI to transform learning and improve educational outcomes.

The Advisory Board members are:

Dan Carroll
Former Chief Product Officer and Co-Founder of Clever

Richard Culatta
CEO of ISTE+ASCD

Bethanie Drake-Maples
Founder and CEO of Atypical AI

Louis Gomez
Professor of Education at UCLA and a Member of the National Academy of Education

Babak Mostaghimi
Founding Partner of LearnerStudio

Amelia Vance
Founder and President of the Public Interest Privacy Center

Advisory board members represent a range of perspectives with expertise in learning science, education technology, data privacy and artificial intelligence. See here for their bios.

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