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Generative AI in teacher education: a systematic review

International Journal of Evaluation and Research in Education (IJERE) · Vol. 15 No. 2 (2026) · hlm. 966–975

DOI
10.11591/ijere.v15i2.37225
ISSN
2620-5440, 2252-8822
Terbit
2026-04-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

This study addresses a critical gap in the literature by conducting one of the earliest systematic reviews (2021-2025) on generative artificial intelligence (GenAI) in teacher education. Using a structured screening and coding process, 35 peer-reviewed articles from Scopus and Web of Science (WoS) were analyzed to examine methodological trends, geographical disparities, and cross-cultural adaptability. The review identifies four major application areas, including stakeholder perception analysis, instructional resource generation, curriculum design, and student-AI collaborative learning, and synthesizes their underlying pedagogical mechanisms. Key findings reveal pronounced geographical imbalance (with no studies from Africa or Latin America), heavy reliance on short-term qualitative designs, and limited empirical or longitudinal validation. Based on these insights, the study proposes a conceptual framework linking GenAI applications, challenges, and future research pathways. This work contributes a structured evidence base and offers guidance for advancing GenAI-integrated teacher education through more rigorous, inclusive, and context-sensitive research.

Kata kunci

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30 penulis · 80 tautan kepenulisan bersama · 41 lintas organisasi

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  • Universitas PGRI Mahadewa Indonesia
  • Muhammadiyah University of Surakarta
  • National Research and Innovation Agency
  • University of Malaya
  • Universitas Jambi
  • Indonesia Open University
  • Universitas Islam Negeri Sumatera Utara
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
SITI HAJAR HALILIAfiliasi tidak diketahui4
Naufal IshartonoMuhammadiyah University of Surakarta3
Amirul MukmininUniversitas Jambi2
Siti Hajar HaliliAfiliasi tidak diketahui1
Adi JufriansahAfiliasi tidak diketahui1
Janu ArlinwibowoNational Research and Innovation Agency1
Muhammad Noor KholidMuhammadiyah University of Surakarta1
Asyifa Nur AfiyahMuhammadiyah University of Surakarta1
Deni HadianaNational Research and Innovation Agency1
I Wayan CitrawanUniversitas PGRI Mahadewa Indonesia1
I Wayan WidanaUniversitas PGRI Mahadewa Indonesia1
I Wayan SumandyaUniversitas PGRI Mahadewa Indonesia1
I Nengah Suka WidanaUniversitas PGRI Mahadewa Indonesia1
Hanief AriefNational Research and Innovation Agency1
Wayan MeterIndonesia Open University1
Syaukani SyaukaniUniversitas Islam Negeri Sumatera Utara1
Siti IsmahaniUniversitas Sumatera Utara1
D Sarasvati DoraisamyUniversity of Malaya1
Li YueboAfiliasi tidak diketahui1
Min Hui LeowAfiliasi tidak diketahui1
Norshahida HassanAfiliasi tidak diketahui1
Sabrina Adia Mohd SharizalAfiliasi tidak diketahui1
Peng RanAfiliasi tidak diketahui1
Shamsul Arrieya AriffinSultan Idris Education University1
Ramlan MustaphaAfiliasi tidak diketahui1
Xu YingyiAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijere.iaescore.com/index.php/IJERE/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Generative AI in teacher education: a systematic review</dc:title>
	<dc:creator>Yuan, Longfa</dc:creator>
	<dc:creator>Razak, Rafiza Abdul</dc:creator>
	<dc:creator>Kamsin, Amirrudin</dc:creator>
	<dc:creator>Abdul-Rahman, Siti-Soraya</dc:creator>
	<dc:subject xml:lang="en-US">Educational technology; generative AI; systematic review; teacher education; teacher training</dc:subject>
	<dc:description xml:lang="en-US">This study addresses a critical gap in the literature by conducting one of the earliest systematic reviews (2021-2025) on generative artificial intelligence (GenAI) in teacher education. Using a structured screening and coding process, 35 peer-reviewed articles from Scopus and Web of Science (WoS) were analyzed to examine methodological trends, geographical disparities, and cross-cultural adaptability. The review identifies four major application areas, including stakeholder perception analysis, instructional resource generation, curriculum design, and student-AI collaborative learning, and synthesizes their underlying pedagogical mechanisms. Key findings reveal pronounced geographical imbalance (with no studies from Africa or Latin America), heavy reliance on short-term qualitative designs, and limited empirical or longitudinal validation. Based on these insights, the study proposes a conceptual framework linking GenAI applications, challenges, and future research pathways. This work contributes a structured evidence base and offers guidance for advancing GenAI-integrated teacher education through more rigorous, inclusive, and context-sensitive research.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-04-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijere.iaescore.com/index.php/IJERE/article/view/37225</dc:identifier>
	<dc:identifier>10.11591/ijere.v15i2.37225</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Evaluation and Research in Education (IJERE); Vol 15, No 2: April 2026; 966-975</dc:source>
	<dc:source>2620-5440</dc:source>
	<dc:source>2252-8822</dc:source>
	<dc:source>10.11591/ijere.v15i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijere.iaescore.com/index.php/IJERE/article/view/37225/14938</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Longfa Yuan, Rafiza Abdul Razak, Amirrudin Kamsin, Siti-Soraya Abdul-Rahman</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>