The DOI matches its Crossref record.
Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
AI in Education: A Review of Personalized Learning and Educational Technology
Ayeni, O. O., Hamad, N. M. A., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024) · GSC Advanced Research and Reviews · doi:10.30574/gscarr.2024.18.2.0062 · Cited by 436
Barrett, A., & Pack, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00427-0 · Cited by 391
Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107798 · Cited by 336
Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024) · Education Sciences · doi:10.3390/educsci14101090 · Cited by 177
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. https://app.cochat.ai/papers/10.1111/jcal.12577
- The paper
- 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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.