Learning Commons joined thousands of educators, researchers, and developers at the ASU+GSV Summit in San Diego to explore how to ensure AI-powered learning tools are built on quality, rigor, and trust.
At panels, hands-on sessions, and community convenings, we engaged in conversations shaping the future of education technology — from the role of learning science and productive struggle, to creativity in an AI-enabled world, to the data and infrastructure needed to support better instruction at scale.
A few themes emerged from these discussions:
- Demand is growing for rigor and pedagogical integrity in AI-powered learning tools.
Educators are seeking evidence on how these tools can support their vision for instructional excellence. As Sandra Liu Huang, president of Learning Commons, emphasized during the “Raising the Bar: Building Quality, Trust, and Learning Science into AI Education Tools” panel, solutions must be built on a foundation of research about how students learn best. - Evaluating the quality of outputs is a must.
At workshops and hackathons, we heard how challenging it is to assess whether outputs from AI tools are pedagogically sound. We announced early access to two new Literacy Evaluators — Subject Matter Knowledge and Conventionality — designed to close the gap between surface-level readability and real comprehension. Our Evaluators assess the quality of AI outputs for pedagogy and rigor, because teachers deserve tools they can trust. - Scaling education research through shared, open infrastructure can raise the bar for quality across the field.
There is a growing body of research being translated into well-structured, machine-readable datasets that offer high-quality, open resources to anyone building edtech. These datasets include state academic standards, learning science research, and literacy frameworks to help increase the rigor of AI tools in the classroom. For example, Student Achievement Partners shared about our collaboration to turn their qualitative text-complexity rubric into a transparent, machine-scorable form so AI tools can be evaluated against research-backed expectations. Digital Promise held an open house about the K-12 AI Infrastructure Program, which is issuing grants to develop openly shared datasets, models, benchmarks, and other digital public goods to advance the accuracy and relevance of AI in education.
At Learning Commons, we will continue to develop new resources that help build a foundation for quality and invite developers, researchers, and partners to collaborate with us alongside a broader ecosystem, investing in shared infrastructure for the long term. We’re also excited to provide early access to the Learning Commons platform that brings together API endpoints, MCP tools, and SDKs. This summer, we will further expand access, introducing additional features, integrations, and licensing options to support adoption at scale.
We’re grateful to everyone who joined us at ASU+GSV, and we are excited to keep building the infrastructure that helps the field move forward together.