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Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’

Open access
Gardner, John, O'Leary, Michael, Yuan, Li·2021·Journal of Computer Assisted Learning

Abstract

Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligence‐related educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them.

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Metadata

Authors
Gardner, J., O'Leary, M., & Yuan, L.
Venue
Journal of Computer Assisted Learning
Publisher
Wiley
Published
2021-07-05
Volume
37
Issue
5
Pages
1207-1216
ISSN
  • 0266-4909 (ISSN-L)
  • 1365-2729
OpenAlex
W3179330384
Cited by
209
References
0
FWCI
24.77
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. https://app.cochat.ai/papers/10.1111/jcal.12577
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Gardner, J., O'Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12577

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.