Abstract
Artificial intelligence (AI) and other emerging technologies are rapidly transforming educational assessment, creating new opportunities and challenges for designing assessments that recognize and respond to learner variability. This white paper synthesizes insights from the Interweaving Assessment convenings, a series of three in-person gatherings facilitated by Menlo Education Research that brought together researchers, policymakers, practitioners, and technology developers to examine the future of assessment. Particular attention is given to the first convening’s focus on AI, accessibility, and learner variability. Drawing on discussions across the convenings, this paper identifies key research and development priorities for advancing assessment innovation through the lens of learner variability, while also highlighting critical cautions, tensions, and unresolved questions related to AI-enabled assessment. The paper concludes by outlining enabling conditions and policy levers that can support responsible innovation at the intersections of assessment, AI, accessibility, and learner variability, with the goal of fostering assessment systems that are more accessible, fair, and valid.
Cite As
CAST. (2026). Interweaving assessment in the age of AI: Policy implications for advancing innovation that centers learner variability. CAST.
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