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TRUVACE RECORD VERSION
record: TRV-2026-2241
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-24T18:34:28.711322Z
status: published
lens: p_space
sector: science
headline: Artificial Intelligence, Ethics, Academic and Research Integrity in Higher Education: A Bibliometric Analysis
dek: Artificial intelligence (AI) offers considerable benefits to higher education, yet its adoption raises persistent concerns about ethics and academic and research integrity. Few studies have mapped this intersection bibliometrically. This study, therefore, analysed 213 journal articles published between 2015 and 2025 and indexed in Scopus and Web of Science, with record selection reported in accordance with PRISMA 2020. VOSviewer was used to examine publication trends, leading sources, highly cited studies, produ…
gain_title: (none)
problem_title: Scholarship on AI in higher education shows research integrity is under-examined relative to academic integrity, and African contributions remain limited, leaving gaps in governance evidence.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Scholarship on AI in higher education shows research integrity is under-examined relative to academic integrity, and African contributions remain limited, leaving gaps in governance evidence.
problem_evidence: research integrity remains under-examined relative to academic integrity | African contributions remained limited
quick_read: A bibliometric review mapped 213 journal articles from 2015 to 2025, drawn from Scopus and Web of Science, on how AI intersects with ethics and integrity in universities. Output was negligible until 2023 and then rose sharply, with the United States, China and the United Kingdom producing the most work.

The pattern matters because universities are adopting AI faster than the evidence base on its integrity risks has matured. The gaps in research-integrity studies and in African-led work mean institutional policies may rest on uneven evidence, and the analysis cannot show how well any governance approach works in practice.
limitation: The study maps publication patterns rather than measuring real-world outcomes of AI use, and it finds that research integrity is under-examined and that empirical work is still needed.
tag: Evidence-backed problem
key_points: The analysis covered 213 journal articles published between 2015 and 2025 and indexed in Scopus and Web of Science. | Of 845 harmonised keywords, 20 met the occurrence threshold and formed four clusters, including AI research ethics and privacy and AI and academic integrity. | Recent scholarship has shifted toward generative AI, AI literacy and educational technology. | The authors call for institutional AI governance frameworks, AI-literacy development, and more empirical and African-led research.
rundown: The study followed PRISMA 2020 for reporting record selection and used VOSviewer to examine publication trends, leading sources, highly cited studies, productive countries, keyword co-occurrence and how themes changed over time.

Four thematic clusters emerged: AI research ethics and privacy; responsible generative AI and literacy; AI and academic integrity; and AI-enabled teaching and educational technology.

Geographically, the United States, China and the United Kingdom led production while African contributions stayed limited, which the authors use to argue for more African-led research alongside governance and literacy efforts.
sources:
- peer_reviewed | Journal of Information Systems and Informatics | https://doi.org/10.63158/journalisi.v8i5.1763 | 2026-10-24
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