Developers can now use Knowledge Graph to build tools that support durable skills instruction
VIEW IN BROWSER ([link removed] )
Learning Commons logo ([link removed] )
News & Updates
Hello,
Learning Commons is excited to announce new collaborations with the Carnegie Foundation for the Advancement of Teaching ([link removed] ) and XQ Institute ([link removed] ) to expand the Knowledge Graph ([link removed] ) with durable skills datasets. Both the Carnegie Foundation and XQ Institute bring deep field expertise in learning science to this work. These two research-backed frameworks provide the foundational rigor necessary to transform high-level educational aspirations into tangible indicators for student growth.
Twenty-six states and hundreds of school districts have officially adopted a Portrait of a Graduate, a framework that defines the durable skills students should develop to thrive in and out of school. Durable skills, such as collaboration, communication, leadership, and critical thinking, remain valuable throughout education, careers, and life. These competencies serve as the bedrock of student development, equipping learners to connect academic knowledge to real-world application. While articulating this vision is a crucial first step, establishing and utilizing shared research-backed definitions and progressions to understand how these skills develop, what helps them grow, and what proficiency looks like is critical for the field. This is where the Carnegie Foundation and XQ Institute's durable skills frameworks come in; they offer the research-aligned definitions and progressions schools need to achieve this vision.
Developers can now access these research-backed durable skills frameworks through Knowledge Graph in an open, machine-readable format, making it easier to build tools that help educators support students in developing durable skills. These datasets translate high-level goals into actionable indicators, enabling education tools to more precisely align pedagogical materials with student skill development and support educators in measuring progress toward those durable skills.
Datasets now available
XQ Competencies ([link removed] ) (Grades 9–12): A framework of 37 competencies defining the cognitive, social, and affective capacities students need for college, career, and life. Each competency includes teachable component skills and four-level learning progressions that describe how proficiency develops. Designed to integrate with academic state standards, the framework helps developers create learning experiences and resources that support the development and assessment of academic and durable skills together. For example, a curriculum builder could create a project in which students apply mathematical reasoning to solve problems while also developing the ability to negotiate disagreements and reach decisions.
Carnegie Skills Progressions ([link removed] ) (Grades 9–12): Research-grounded definitions that offer a clear roadmap for how durable skills grow in sophistication over time. By breaking down complex skills into subskills and actionable indicators, these science-based definitions allow developers to build tools that help educators easily plan instruction, design assessments, and provide clear, meaningful feedback on student progress on the targeted skill.
Since the Knowledge Graph is queryable by framework, developers can layer these competencies onto state academic standards and align them with a specific school district's Portrait of a Graduate without tedious manual customization or the need to develop their own skill definitions and progressions.
The durable skills dataset builds upon the current data sets in Knowledge Graph, including state academic standards, evidence-based curricula, and learning progressions. All resources created through our XQ Institute and Carnegie Foundation partnerships are openly available for developers to build on, reflecting our commitment to building public infrastructure for AI in education.
Try out the durable skills datasets in Knowledge Graph
Developers can access these open, machine-readable datasets directly through the link below to integrate them into their tools.
Access the durable skills datasets
([link removed] )
Thank you for the work you do to make learning better for students everywhere.
Warmly,
Sandra Liu Huang
CEO, Learning Commons
Learning Commons logomark ([link removed] )
Learning Commons | A proud part of the Chan Zuckerberg Initiative
2682 Middlefield Road, Suite i, Redwood City, CA 94063
Manage Preferences ([link removed] ) | Unsubscribe ([link removed] ) | Contact us (mailto:
[email protected]) | Learn more ([link removed] ) | Follow us on LinkedIn ([link removed] )
