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Cited by 2

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Academic Integrity and Generative AI Research for Higher Education: A Systematic Literature Review

University of Latvia, Matjola, Ieva·2025·Human, Technologies and Quality of Education, 2025
DOI 10.22364/htqe.2025.25
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Abstract

Since the spring of 2023, when generative AI (GenAI) tools became available to the public and their use became widespread, the question of academic integrity in educational processes has been raised. GenAI has created conditions in which new situations arise at higher education levels (and eventually at other levels of education), and teaching and learning, as well as assessment, can be carried out with the help of GenAI. This review offers a detailed insight into the current research landscape and outlines future research needs to promote academic integrity and the ethical use of GenAI in higher education. By assessing articles published in 2023–2025 and indexed in the Web of Science and Scopus databases, the review identifies the methods, research designs, and samples used, summarises the main directions for potential future research, and identifies research gaps. The systematic literature review identifies the qualitative, quantitative, and mixed methods used in the study, such as interviews, surveys, case studies, and experimental designs. Study samples typically included students and academic staff, but often lacked cross-cultural perspectives and studies that explored academic integrity issues in depth. Research gaps were also identified, such as insufficient attention to the specificities of different disciplines and the long-term impact of GenAI technology.

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Metadata

Authors
University of Latvia, & Matjola, I.
ORCID
Matjola, Ieva
Venue
Human, Technologies and Quality of Education, 2025
Publisher
University of Latvia Press
Published
2025-11
Pages
358-371
DOI
10.22364/htqe.2025.25
Cited by
2
References
3

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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.