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All Journal Immortalis Journal of Interdisciplinary Studies Innovasi : Jurnal Inovasi Pendidikan
Febry Suprapto
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Trends in ChatGPT and Generative Artificial Intelligence in Education Research: A Scopus Based Analysis Miftahul Jannah; Benny Prasetiya; Febry Suprapto; Khoiriyah
Immortalis Journal of Interdisciplinary Studies Vol. 2 No. 1 (2026): January - March
Publisher : PT. Caesarindo Triloka Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/sv5x7870

Abstract

The emergence of Generative Artificial Intelligence (GenAI) technologies such as ChatGPT has transformed the landscape of education research, reshaping how knowledge is created, accessed, and disseminated. This study aims to analyze global research trends on ChatGPT and GenAI in education through a bibliometric analysis of Scopus-indexed publications from 2023 to 2025. Using a quantitative bibliometric approach with data visualization tools such as VOSviewer and Bibliometrix (R package), this research examines publication growth, scientific collaboration, influential authors and journals, and keyword co-occurrence networks. The PRISMA 2020 protocol was applied to ensure systematic and transparent data selection. The results show an exponential increase in research output on GenAI in education, with the United States, China, and Australia leading contributions. Thematic mapping reveals three dominant clusters: (1) ChatGPT and generative AI applications in higher education; (2) human-centered and ethical AI in learning; and (3) large language models (LLMs) in educational innovation. This study identifies ChatGPT as both a learning tool and an epistemic partner that enhances personalization, creativity, and efficiency in teaching and research. The novelty of this research lies in its development of the “Generative Education Ecosystem (GEE)” framework, integrating socio-technical systems and constructivist theories to explain human-AI collaboration in learning. The findings contribute empirically and theoretically to understanding the global trajectory of GenAI in education and provide strategic insights for policymakers and educators to foster adaptive, ethical, and sustainable AI-based learning systems.
The Intellectual Structure of Learning Analytics Research: A Bibliometric and Science Mapping Analysis Sumiyati; Benny Prasetiya; Febry Suprapto; Khoiriyah
Immortalis Journal of Interdisciplinary Studies Vol. 2 No. 1 (2026): January - March
Publisher : PT. Caesarindo Triloka Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/hf2jpy61

Abstract

This study explores the intellectual structure of learning analytics (LA) research through a bibliometric and science mapping analysis. Over the past decade, LA has gained significant importance due to its role in analyzing learning behaviors through big data, artificial intelligence, and online learning systems, especially during the COVID-19 pandemic. Using data from Scopus and Web of Science, this study examines trends in publication volume, research themes, international collaborations, and influential authors and institutions within the field from 2020 to 2025. The results reveal key research domains such as educational data mining, MOOCs, self-regulated learning, and AI-integrated adaptive learning systems. Additionally, the analysis uncovers evolving research trends, particularly a shift from MOOCs to learning management systems and AI applications in post-pandemic education. By applying co-citation and co-word analysis, the study identifies emerging themes and their relationships, offering a comprehensive visualization of the field’s intellectual network. The findings also highlight the need for future research on AI-driven learning analytics and ethical considerations. This study contributes to a better understanding of LA's conceptual evolution and offers insights to policymakers, educational institutions, and researchers, helping guide future directions for digital education and data-driven educational policies. The results align with global objectives such as Sustainable Development Goal 4 (quality education for all), promoting the integration of learning analytics to enhance educational quality and inclusivity worldwide.
Co-Authors , Benny Prasetiya , Khoiriyah A. Ansorullah Ahmad Mujahidin Amaliatul Fitriyah Annisa Maryam Nurrohmah Benny Prasetiya Daud Ridwan Emilia Nurwati Fahim Abu Romadlan Hasnun Umar Hera Hapsari Kusuma Khoiriyah Masnu’atul Khoiriyah MIFTAHUL JANNAH Mohammad Nahrawi Musleh Sumiyati Wahyudi Mutalib