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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.

References

  1. 1

    Luckin, R., Holmes, W., Griffiths, M. & Forcier, L. B. Intelligence Unleashed: An Argument for AI in Education (Pearson, 2016); http://go.nature.com/2jwF0zx

    Google Scholar 

  2. 2

    Bostrom, N. & Yudkowsky, E. in Cambridge Handbook of Artificial Intelligence (eds Frankish, K. & Ransey, W. M. ) 316–334 (Cambridge Univ. Press, 2011).

    Google Scholar 

  3. 3

    Self, J. Int. J. Artif. Intell. Educ. 10, 350–364 (1999).

    Google Scholar 

  4. 4

    Hill, P. & Barber, M. Preparing for a Renaissance in Assessment (Pearson, 2014).

    Google Scholar 

  5. 5

    Mavrikis, M. Int. J. Artif. Intell. Tools 19, 733–753 (2010).

    Article  Google Scholar 

  6. 6

    Luckin, R. & du Boulay, B. Int. J. Artif. Intell. Educ. 26, 416–430 (2016).

    Article  Google Scholar 

  7. 7

    Bull, S. & Kay, J. Int. J. Artif. Intell. Educ. 17, 89–120 (2007).

    Google Scholar 

  8. 8

    Spector, M. & Ramsey, M. U.S. proposes spending $4 billion to encourage driverless cars. The Wall Street Journal (14 January 2016); http://go.nature.com/2jZePEM

  9. 9

    Toyota will establish new artificial intelligence research and development company. Toyotahttp://bit.ly/2jRt1gW (5 November 2015).

  10. 10

    Memorandum Submitted by Association of School and College Leaders (ASCL) (UK Parliament, 2007); http://go.nature.com/2jpIBBN

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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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