Rethinking Assessment In Education: How AI And Cognitive Science Improve Learning

"Learning science has long shown that assessment in education supports learning best when it actively shapes practice-guiding what needs to be revisited, how difficulty progresses, and when learners are ready to move on. Evidence from research [1] shows that repeated low-stakes retrieval practice significantly improves long-term retention and transfer of learning, positioning assessment itself as a driver of learning rather than a mere measurement tool."

"Historically, building such systems in production has been costly and complex, as adaptive sequencing, persistent learner models, and frequent low-stakes assessment demand significant manual effort. AI now makes this practical by dynamically generating questions, updating learner models, and enabling continuous, low-overhead assessment at scale. Despite those technical gains, most platforms still haven't put a tightly integrated, AI-driven assessment in education into routine practice."

Assessment should actively shape practice by indicating what to revisit, how difficulty should progress, and when learners are ready to advance. Repeated low-stakes retrieval practice significantly improves long-term retention and transfer. Adaptive sequencing, persistent learner models, and frequent low-stakes assessment have historically required costly manual effort. AI enables dynamic question generation, automated learner-model updates, and continuous, low-overhead assessment at scale. Platforms still largely lack tightly integrated AI-driven assessment in routine practice. Near-term opportunities include increased efficiency through automation, scalable generation and calibration of items, draft rubrics, and first-pass evaluation while keeping humans in validation roles.

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