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Emerging Dynamics of ChatGPT in Academia: A Scoping Review

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Mahrishi, Mehul, Abbas, Asad, Radovanović, Danica, Hosseini, Samira·2024·Journal of University Teaching and Learning Practice
DOI 10.53761/b182ws13
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Abstract

Large Language Models (LLMs) and Generative AI tools are revolutionising every aspect of academia, including medical, physical, and STEM education. They have also proved their mettle in blended learning systems and distance learning programs by improving educational sustainability, accessibility, and engagement. Through this scoping review, we aim to provide an essential overview of the state of the art of ChatGPT from the standpoint of exploring its development, analysing its current trajectory, and emerging dynamics in active research responsible for defining regulations and protocols. The PRISMA benchmarking was used on the Scopus dataset, with 109 papers from 2022 to 2023. Interactive results and bibliographic maps are generated using the Bibliometrix library of R-Studio. The findings are aligned with the research questions and represent exceptional growth in scientific production. Furthermore, relevant avenues for research publications, leading countries, and institutions in the area are also listed. The thematic and trends analysis anticipated that artificial intelligence and generative AI will substantially influence nearly every dimension in the coming years. The review also identified the possible advantages and disadvantages of adopting ChatGPT in higher education and analysed its deployment, considering ethical issues. The research concludes that, apart from ChatGPT, other large language models are also transforming artificial intelligence in education. However, ethical concerns and implications in education highlight vital issues for further research to ensure AI's responsible and ethical use.

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  • The literature increasingly recognises that effective AI use requires more than access; it demands skill, including prompt engineering, interpreting AI-generated results, and recognising limitations or biases.

    “The literature increasingly recognises that effective AI use requires more than access, it demands skill. Students must learn how to use AI tools confidently and competently, which includes understanding prompt engineering, interpreting AI-generated results, and recognising the limitations or biases inherent in these systems (Dolenc & Brumen, 2024; Haq et al., 2025; Mahrishi et al., 2024).”

    From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Mahrishi et al., 2024

    The Practical Implications section says educators may need stronger digital literacy skills before helping students interact effectively with AI, and the Discussion identifies biased information as a risk. This supports the general need for skills and awareness of limitations, but the paper does not specifically say students must learn prompt engineering or how to interpret AI-generated results. Page numbers were not available in our copy. (LLM, conf=0.86)

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Cited by

Sorted by citations

Authors
Mahrishi, M., Abbas, A., Radovanović, D., & Hosseini, S.
Affiliations
  • Tecnológico de Monterrey (MX)
  • GESIS - Leibniz Institute for the Social Sciences (DE)
ORCID
Venue
Journal of University Teaching and Learning Practice
Publisher
Open Access Publishing Association
Published
2024-03-12
Volume
21
Issue
1
ISSN
1449-9789 (ISSN-L)
DOI
10.53761/b182ws13
OpenAlex
W4392706454
Cited by
28
References
30
Citations by year
2026: 12 · 2025: 11 · 2024: 5
FWCI
10.56
Type
journal article
Language
en
Open access
Licence
Locations
Journal of University Teaching and Learning Practice (publishedVersion, cc-by-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
ChatGPT, large language models, generative AI in education, higher education, AI ethics, responsible AI use, blended learning, distance education, educational technology, research trends

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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