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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