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ARTIFICIAL INTELLIGENCE AND BIAS IN EDUCATION: A BIBLIOMETRIC ANALYSIS OF GLOBAL TRENDS, KNOWLEDGE STRUCTURES, AND RESEARCH GAPS

Jurnal Ilmiah Ilmu Terapan Universitas Jambi · Vol. 10 No. 3 (2026) · hlm. 1202–1220

DOI
10.22437/jiituj.v10i3.54424
ISSN
2580-2259, 2580-2240
Terbit
2026-06-01
Penerbit
LPPM Universitas Jambi
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

As AI systems increasingly govern assessment, feedback, and learning pathways in education, concerns over algorithmic bias have expanded from technical issues to governance and equity. However, limited attention has been given to how the research landscape itself frames these bias-related issues. This study addresses that gap through a bibliometric analysis of 445 Scopus-indexed publications (1975–April 2025) using Biblioshiny. The results reveal a sharp rise in publications since 2019, with research concentrated in technologically advanced countries. Dominant themes include machine learning, ethics, and medical education, while equity-oriented topics such as gender bias, vocational education, and contextual disparities remain markedly underrepresented. The growing adoption of generative AI further complicates bias dynamics by introducing opacity and amplifying dataset biases. The discussion highlights that geographical and thematic concentration suggests a narrow institutional perspective, potentially marginalizing bias concerns from lower-resource contexts and creating a disconnect between technical AI research and educational justice frameworks. The novelty of this study lies in its meta-level examination of how the research field structures bias priorities, empirically revealing thematic omissions and identifying generative AI as an amplifying factor in educational bias. Collectively, these findings provide a structural foundation for developing more transparent, accountable, and equity-driven AI practices in education.

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  • Bandung Institute of Technology
  • Yogyakarta State University
  • Universitas Sumatera Utara
  • Universitas Airlangga
  • Universitas Halu Oleo
  • The University of Tokyo
  • Andalas University
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Rekan penulisOrganisasiArtikel bersama
Mar’atus SholikahAfiliasi tidak diketahui2
Fajri Anugerah Pramadhana KornelBandung Institute of Technology1
Naufal Rafi HibatullahBandung Institute of Technology1
Fahmi FahmiUniversitas Sumatera Utara1
Erwin SutantoUniversitas Airlangga1
Muhammad YazidUniversitas Halu Oleo1
Muhammad AzizThe University of Tokyo1
Muhammad Imran HamidAndalas University1
Mitra SofiyatiBandung Institute of Technology1
Fandi A. WiranataBandung Institute of Technology1
Eueung MulyanaBandung Institute of Technology1
Isa AnshoriBandung Institute of Technology1
Ardianto SatriawanBandung Institute of Technology1
Hanif Ramadhan Fahmi PutraAfiliasi tidak diketahui1
Muhammad Ari KusumaAfiliasi tidak diketahui1
Faizal AnsyoriAfiliasi tidak diketahui1
Umi HidayatiAfiliasi tidak diketahui1
Rosidah RosidahState University of Semarang1
Wahyuningtyas Nilam SariYogyakarta State University1
Mar’atus SholikahYogyakarta State University1
Febrika Yogie HermantoYogyakarta State University1
Berlina HidayatiUniversitas Gadjah Mada1

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Sumber metadata: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/JIITUJ/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">ARTIFICIAL INTELLIGENCE AND BIAS IN EDUCATION: A BIBLIOMETRIC ANALYSIS OF GLOBAL TRENDS, KNOWLEDGE STRUCTURES, AND RESEARCH GAPS</dc:title>
	<dc:creator xml:lang="en">Sutirman, Sutirman</dc:creator>
	<dc:creator xml:lang="en">Yuliansah, Yuliansah</dc:creator>
	<dc:creator xml:lang="en">Shalannanda, Wervyan</dc:creator>
	<dc:creator xml:lang="en">Hemanto, Febrika Yogie</dc:creator>
	<dc:creator xml:lang="en">Febrianto, Indra</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">AI</dc:subject>
	<dc:subject xml:lang="en">Bias</dc:subject>
	<dc:subject xml:lang="en">Education</dc:subject>
	<dc:subject xml:lang="en">Ethict</dc:subject>
	<dc:description xml:lang="en">As AI systems increasingly govern assessment, feedback, and learning pathways in education, concerns over algorithmic bias have expanded from technical issues to governance and equity. However, limited attention has been given to how the research landscape itself frames these bias-related issues. This study addresses that gap through a bibliometric analysis of 445 Scopus-indexed publications (1975–April 2025) using Biblioshiny. The results reveal a sharp rise in publications since 2019, with research concentrated in technologically advanced countries. Dominant themes include machine learning, ethics, and medical education, while equity-oriented topics such as gender bias, vocational education, and contextual disparities remain markedly underrepresented. The growing adoption of generative AI further complicates bias dynamics by introducing opacity and amplifying dataset biases. The discussion highlights that geographical and thematic concentration suggests a narrow institutional perspective, potentially marginalizing bias concerns from lower-resource contexts and creating a disconnect between technical AI research and educational justice frameworks. The novelty of this study lies in its meta-level examination of how the research field structures bias priorities, empirically revealing thematic omissions and identifying generative AI as an amplifying factor in educational bias. Collectively, these findings provide a structural foundation for developing more transparent, accountable, and equity-driven AI practices in education.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2026-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/JIITUJ/article/view/54424</dc:identifier>
	<dc:identifier>10.22437/jiituj.v10i3.54424</dc:identifier>
	<dc:source xml:lang="en">Jurnal Ilmiah Ilmu Terapan Universitas Jambi; Vol. 10 No. 3 (2026): Volume 10, Nomor 3, June 2026; 1202-1220</dc:source>
	<dc:source>2580-2259</dc:source>
	<dc:source>2580-2240</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/JIITUJ/article/view/54424/24605</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Sutirman Sutirman, Yuliansah Yuliansah, Wervyan Shalannanda, Febrika Yogie Hemanto, Indra Febrianto</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>