Rethink for Whom, What, and How We Measure

This video explores how multimodal AI is reshaping what we can observe about learning and work, and why getting those inferences right matters for high-stakes decisions. As AI-powered tools expand to include speaking, listening, handwriting, drawing, and tangible interactions, new possibilities are emerging to better capture what learners know and can do. It challenges outdated approaches to bias, safety, and measurement, and invites rethinking how we design AI systems that are high-quality, fair, and safe.

Innovating What We Measure in the AI Era

With emerging scientific and technological breakthroughs, there is an opportunity to dramatically expand the field’s ability to measure complex yet vital competencies and contexts. Assessments signal priorities and focus thinking about what’s important, influencing what’s taught and learned. 

Innovating For Whom We Measure in the AI Era

In an era of multi-modal AI, the opportunity exists to center the needs of learners, educators, families, and the wider community in ways unthought of previously. In addition to addressing how the sciences of learning, development, measurement, and improvement are being leveraged to fuel innovation, this panel surfaces lessons in usefulness and usability and data-driven feedback and decisions.

Upgrading the Quality and Explainability of Educational Measurement in the AI Era

As innovative technologies (including multi-modal AI) transform assessment, the field must remain attuned to the notion of quality and to acting in ways that grow the credibility and informed embrace of new assessments. This webinar focuses on particular components of quality as defined by K-12 user-informed principles, including Scientific Soundness in the AI era and transparency and trust. 

Volume I: Foundations for Assessment in the Service of Learning

This session focused on unpacking Volume I: Foundations for Assessment in the Service of Learning. During the session, Volume I Authors Scott Marion, Kristen Huff, and Mario Piacentini joined us to discuss work from the learning sciences, measurement theory, and improvement science to offer principled design and conceptual frameworks for integrating assessment with teaching and learning.

Volume II: Reconceptualizing Assessment to Improve Learning in the Age of AI

 This session explored Volume II: Reconceptualizing Assessment to Improve Learning in the Age of AI. We were joined by Susan Brookhart, Teanna Feng, Neal Kingston, Maria Elena Oliveri, and Stephen Sireci discussed how assessment can move from foundational principles to the conceptual tools and methods needed to build assessment systems that actively improve, not just measure, learning.

Volume III: Examples of Assessment in the Service of Learning

Girlie Delacruz, Howard Everson, Sunil Gunderia, James Pellegrino, and Jeremy Roberts joined us to discuss how assessment can become a catalyst for learning given the affordances of AI. We explored how assessment can move beyond sorting and ranking to serve as a resource for learning. 

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Handbook for Assessment in the Service of Learning Design Principles

During this session, we unpacked the big ideas, design principles, and practical takeaways from the Handbook for Assessment in the Service of Learning series.

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