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Cited by 145Artificial Intelligence in EducationDigital Education and SocietyArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Generative AI and higher education: a review of claims from the first months of ChatGPT

Open access
Jensen, Lasse X, Buhl, Alexandra, Sharma, Anjali, Bearman, Margaret·2025·Higher Education
DOI 10.1007/s10734-024-01265-3
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Abstract

Abstract The release of the Artificial Intelligence (AI) chatbot ChatGPT renewed discussions about how AI would upend higher education. This paper presents a critical analysis of “grey literature” claims made in the first months after ChatGPT was made public, exploring what these discussions might mobilise in practice. We identified articles for inclusion through a systematic search of five prominent higher education sector outlets. The included articles were thematically coded for claims about generative AI and higher education. We identified ten claims: Three about the nature of ChatGPT, four about changing practices of institutions and teachers, and three about new independent practices of students. Overall, the claims present a positive perspective on AI in higher education. While being perceived as a disruption of the status quo, the authors generally frame AI as a catalyst for existing agendas, e.g. assessment reform, personalisation, or inclusion. This suggests a focus on embracing the affordances offered by AI and primarily addressing risks by including AI in curricula. Furthermore, the claims mainly portray students as either plagiarists or victims of a failing educational system. The paper proposes that a more critical interrogation of generative AI, and the involvement of students in the conversation, may be beneficial.

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Research works printed but not linked: 2

  1. Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459. https://doi.org/10.3102/2F00346543053004445 · DOI
  2. Saunders, S. (2023). Rather than ban generative AI, universities must learn from the past. University World News. https://www.universityworldnews.com/post.php?story=20230221075136410. Accessed 28 Feb 2023.

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Metadata

Authors
Jensen, L. X., Buhl, A., Sharma, A., & Bearman, M.
Affiliations
  • University of Copenhagen (DK)
  • Public Health Foundation of India (IN)
  • Deakin University (AU)
Corresponding author
Jensen, Lasse X
ORCID
Venue
Higher Education
Publisher
Springer Science and Business Media LLC
Published
2024-07-15
Volume
89
Issue
4
Pages
1145-1161
ISSN
  • 0018-1560 (ISSN-L)
  • 1573-174X
DOI
10.1007/s10734-024-01265-3
OpenAlex
W4400659622
Cited by
144
References
25
Citations by year
2026: 76 · 2025: 65 · 2024: 3
FWCI
54.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Higher Education (publishedVersion, cc-by)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
Funders
Novo Nordisk Fonden (NNF22SH0078207)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Education › Digital Education and Society
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI, ChatGPT, artificial intelligence in education, institutional practices, teaching practice, student agency, assessment reform, personalization, inclusion, student plagiarism, critical perspectives on AI

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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.

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.

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