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Cited by 59

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

A Cross-Disciplinary Examination of the Instructional Uses of ChatGPT in Higher Education

Onal, Sinan, Kulavuz-Onal, Derya·2024·Journal of Educational Technology Systems
DOI 10.1177/00472395231196532
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Abstract

This paper examines the potential uses of ChatGPT in generating assessment tasks that can be used across different disciplines in higher education. To illustrate this, we provide examples from three courses in the disciplines of industrial engineering and applied linguistics: Project Analysis and Control, Manufacturing Processes, and Introduction to Linguistics. Our examination of ChatGPT focuses on how some of the common, but time-consuming, tasks can be generated by using ChatGPT as a supportive instructional tool. We observed in our analysis that ChatGPT demonstrates a high level of performance in generating assessment questions and tasks that are accurate and on-topic, and a level of creativity and flexibility in its question generation capabilities. However, it is important to note that ChatGPT is not designed to replace human expertise or judgment. It is crucial that instructors carefully evaluate the reliability and accuracy of the assessments or information generated by ChatGPT.

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Crossref · 10.1177/00472395231196532

Cites

41 of 41 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 17

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Cited by

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Metadata

Authors
Onal, S., & Kulavuz-Onal, D.
Affiliations
  • Southern Illinois University, Edwardsville
  • Salisbury University
ORCID
Onal, Sinan
Venue
Journal of Educational Technology Systems
Publisher
SAGE Publications
Published
2023-09-06
Volume
52
Issue
3
Pages
301-324
ISSN
  • 0047-2395
  • 1541-3810
DOI
10.1177/00472395231196532
Cited by
59
References
41
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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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.

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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.