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Towards artificial intelligence-based assessment systems

Nature Human Behaviour volume 1, Article number: 0028 (2017) Cite this article

‘Stop and test’ assessments do not rigorously evaluate a student's understanding of a topic. Artificial intelligence-based assessment provides constant feedback to teachers, students and parents about how the student learns, the support they need and the progress they are making towards their learning goals.

Decades of research have shown that knowledge and understanding cannot be rigorously evaluated through a series of 90-minute exams. The prevailing exam paradigm is stressful, unpleasant, can turn students away from education, and requires that both students and teachers take time away from learning. And yet we persist globally to rely on these blunt instruments, sending students off to universities and the workplace ill-equipped for their futures.

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Figure 1: A simple Open Learner Model for tracking how a child is using the help facilities of a piece of science software.

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  1. Rose Luckin is Professor of Learner Centred Design, UCL Knowledge Lab, Institute of Education, University College London, 23–29 Emerald Street, London WC1N 3QS, UK.,

    Rose Luckin

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  1. Rose Luckin
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Correspondence to Rose Luckin.

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Luckin, R. Towards artificial intelligence-based assessment systems. Nat Hum Behav 1, 0028 (2017). https://doi.org/10.1038/s41562-016-0028

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