The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
AI: new source of competitiveness in higher education
Abstract
Purpose This paper aims to survey the current landscape of artificial intelligence (AI) applications in higher education institutions (HEIs) and recommend future directions. Design/methodology/approach This paper reviews the recent trends, showcases the applications and provides future directions through a review of current uses of AI in HEIs. Findings The results of this study highlight successful applications of AI technologies in three main areas of college operation: student learning experience; student support; and enrollment management. Research limitations/implications This review has important implications for early adopters of AI by HEIs in providing a competitive advantage. The limitation lies in the scope of the review. It is not comprehensive and does not cover other areas of college operations. Originality/value This is the first review about AI in higher education. It is of value in building future research and serving as a framework for AI applications in HEI.
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“Echoing this concern, Hannan and Liu [7] discuss the increase in cheating and plagiarism due to the ease of access to personalized content and automated grading facilitated by GenAI.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [7]
In the citing paper
“However, they note the benefits of GenAI-powered proctoring solutions that may mitigate academic dishonesty in online courses [7].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [7]
In the citing paper
“Existing literature highlights significant concerns about the potential for GenAI to facilitate academic dishonesty [5,7].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [7]
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Metadata
- Authors
- Hannan, E., & Liu, S.
- Affiliations
- SUNY New Paltz (US)
- ORCID
- Liu, Shuguang
- Venue
- Competitiveness Review: An International Business Journal
- Publisher
- Emerald
- Published
- 2021-08-09
- Volume
- 33
- Issue
- 2
- Pages
- 265-279
- ISSN
- 1059-5422 (ISSN-L)
- OpenAlex
- W3189189854
- Cited by
- 172
- References
- 8
- Citations by year
- 2026: 32 · 2025: 66 · 2024: 49 · 2023: 23 · 2022: 2
- FWCI
- 16.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Competitiveness Review An International Business Journal incorporating Journal of Global Competitiveness (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Digital Transformation of Higher Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, higher education institutions, student learning experience, student support services, enrollment management, institutional competitiveness
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 ›
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