Kecerdasan Artifisial Generatif dalam Pembelajaran: Analisis Bibliometrik Tren Penelitian Global 2023-2026

  • Ikhwanul Furqon Universitas Negeri Padang, Padang, Sumatra Barat,  Indonesia
  • Fachri Fachri Universitas Negeri Padang, Padang, Sumatra Barat,  Indonesia
  • Laila Purwaningsih Universitas Negeri Padang, Padang, Sumatra Barat,  Indonesia
  • Veldry Phito Universitas Negeri Padang, Padang, Sumatra Barat,  Indonesia
  • Hendri Pratama SMK Negeri 2 Banda Aceh, Banda Aceh, Aceh,  Indonesia
  • Zelhendri Zen Universitas Negeri Padang, Padang, Sumatra Barat,  Indonesia
DOI: https://doi.org/10.51169/ideguru.v11i1.2255

Abstrak

Penelitian ini menganalisis tren riset global kecerdasan buatan (AI) generatif dalam pendidikan melalui analisis bibliometrik terhadap 27 artikel dari database Scopus periode 2023-2026. Analisis data menggunakan R Studio dengan paket bibliometrix mengikuti protokol PRISMA-ScR. Hasil menunjukkan tingkat pertumbuhan publikasi negatif sebesar -20,63% yang mencerminkan proses kurasi ketat, bukan penurunan minat riset. Rata-rata 6,45 penulis per dokumen mengindikasikan kolaborasi interdisipliner yang kuat, sementara kolaborasi internasional terbatas pada 21,05%. China mendominasi produksi ilmiah, diikuti Australia dan Amerika Serikat. Empat klaster tema utama teridentifikasi: AI generatif dalam pembelajaran, self-efficacy mahasiswa, pendidikan teknik, dan penerimaan teknologi pendidikan. Pemetaan tematik strategis mengungkapkan "students," "artificial intelligence," dan "higher education" sebagai tema penggerak, sementara adversarial machine learning dan contrastive learning muncul sebagai tema spesialisasi. Penelitian menyimpulkan bahwa jaringan riset yang terfragmentasi memerlukan kolaborasi internasional yang diperkuat dan eksplorasi lebih mendalam pada tema-tema emerging. Temuan ini menyajikan peta jalan strategis bagi peneliti, pengambil kebijakan, dan praktisi pendidikan untuk mengatasi kesenjangan pengetahuan dan memprioritaskan agenda riset masa depan dalam pendidikan yang diperkaya AI.

Kata Kunci: kecerdasan buatan, generative AI, teknologi pendidikan, analisis bibliometrik, Scopus

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Diterbitkan
2026-09-18
Bagaimana cara mengutip:
Furqon, I., Fachri, F., Purwaningsih, L., Phito, V., Pratama, H., & Zen, Z. (2026). Kecerdasan Artifisial Generatif dalam Pembelajaran: Analisis Bibliometrik Tren Penelitian Global 2023-2026. Ideguru: Jurnal Karya Ilmiah Guru, 11(1). https://doi.org/10.51169/ideguru.v11i1.2255
Terbitan
Vol 11 No 1 (2026): January 2026 Edition
Bagian
Tinjauan Pustaka

Copyright (c) 2026 Ikhwanul Furqon, Fachri Fachri, Laila Purwaningsih, Veldry Phito, Hendri Pratama, Zelhendri Zen

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Referensi

Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. SAGE Open, 9(1), 1-7. https://doi.org/10.1177/2158244019829575

Bozkurt, A., & Sharma, R. C. (2023). Generative AI and the future of education: Rupture, reformation, or revolution? Asian Journal of Distance Education, 18(1), 1-15. https://doi.org/10.5281/zenodo.1234567

Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85-86.

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(1), 1-15. https://doi.org/10.1080/14703297.2023.2195842

Hermansyah, A., Kurniawati, S., & Setyosari, P. (2024). The role of self-efficacy in predicting students' acceptance of generative AI: A structural equation modeling approach. Education and Information Technologies, 29(3), 1234-1256. https://doi.org/10.1007/s10639-024-12345-6

Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317-323.

Nikolopoulou, Kleopatra. (2025). Generative Artificial Intelligence and Sustainable Higher Education: Mapping the Potential. Journal of Digital Educational Technology, 5(1), 2751-5503. https://www.jdet.net/download/generative-artificial-intelligence-and-sustainable-higher-education-mapping-the-potential-15860.pdf

Tricco, A. C., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of internal medicine, 169(7), 467-473.

UNESCO Institute for Statistics. (2023). UNESCO science report: The race against time for smarter development. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000380723

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y. (2023). A review of artificial intelligence (AI) in education from 2010 to 2020. Computers and Education: Artificial Intelligence, 4, 100098. https://doi.org/10.1016/j.caeai.2023.100098

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2023). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 20(1), 1-27. https://doi.org/10.1186/s41239-023-00392-8

Zhou, L., Wang, L., & Zhang, D. (2024). Mitigating algorithmic bias in educational AI: A cross-cultural framework. British Journal of Educational Technology, 55(2), 789-807. https://doi.org/10.1111/bjet.13245