CoChat
Sign Up
Cited by 94Artificial Intelligence in EducationAcademic integrity and plagiarismEthics and Social Impacts of AI

Shaping generative AI governance in higher education: Insights from student perception

Open access
Barus, Okky Putra, Hidayanto, Achmad Nizar, Handri, Eko Yon, Sensuse, Dana Indra, Yaiprasert, Chairote·2025·International Journal of Educational Research Open
DOI 10.1016/j.ijedro.2025.100452
Open this paper in CoChat — free account

Abstract

• Students support the integration of generative AI in education. • Students emphasize the importance of AI competency development. • Ethical awareness and misuse prevention are crucial for AI governance. • Students highlight the need for clear guidelines and plagiarism detection. • Academic sanctions for AI ethics violations are supported by students. This study explores student perspectives on generative AI governance in higher education to ensure responsible and ethical AI integration. Employing a mixed-methods approach with an explanatory sequential design, the study gathered data from 111 undergraduate students at Universitas Pelita Harapan through an online survey and semi-structured interviews with 53 students. The study covered key aspects of AI governance, such as ethics, curriculum integration, misuse detection, and academic sanctions, which are crucial for ensuring responsible AI implementation. The findings reveal that students are highly aware of the potential benefits of Generative AI and support its integration into the curriculum. However, they also emphasize the need for clear guidelines, ethics training, and plagiarism detection mechanisms to prevent misuse and uphold academic integrity. These findings provide valuable insights for higher education institutions to develop a comprehensive AI governance framework that balances the potential of Generative AI with the ethical considerations of its use.

Integrity signals

Registry

Crossref · 10.1016/j.ijedro.2025.100452

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

1 retracted

Flagged first, then by citations

1 of the 62 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Barus, O. P., Hidayanto, A. N., Handri, E. Y., Sensuse, D. I., & Yaiprasert, C.
Affiliations
  • Pelita Harapan University (ID)
  • University of Indonesia (ID)
  • Walailak University (TH)
Corresponding author
Barus, Okky Putra
ORCID
Venue
International Journal of Educational Research Open
Publisher
Elsevier BV
Published
2025-06
Volume
8
Pages
100452
ISSN
2666-3740 (ISSN-L)
DOI
10.1016/j.ijedro.2025.100452
OpenAlex
W4407365807
Cited by
93
References
62
Citations by year
2026: 71 · 2025: 22
FWCI
97.50
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Educational Research Open (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Universitas Pelita Harapan (2924/E3/AL.04/2024, P/HK.08.00/2024)
  • Universitas Indonesia
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
generative AI governance, higher education, student perceptions, AI ethics, plagiarism detection, academic sanctions, AI misuse prevention, academic integrity, AI integration in curriculum, ethics training

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 ›

Are you an author, or do you see a problem with this page? Request a review