Mapping the Rise of Generative AI in Teaching and Learning: A Bibliometric Analysis (2014–2024)

Nurfitri Ramadana, Patin Nabilla, Desy Dwi Putri, Rahmatul Ulya

Abstract


This study aims to map the scholarly development of Artificial Intelligence (AI), particularly Generative AI (GenAI), in teaching and learning from 2014 to 2024. The research highlights key trends and the emergence of advanced approaches such as federated learning, contrastive learning, and adversarial learning. A bibliometric method based on PRISMA protocol was applied, using VOSviewer for keyword mapping. Data were extracted from the Scopus database, yielding 8,926 documents. After a systematic screening process, 157 eligible papers were selected for analysis. The findings indicate a significant rise in AI-related educational publications, especially following the emergence of GenAI tools like ChatGPT. Bibliometric visualizations reveal dominant research themes such as e-learning, personalized learning, and chatbots. Meanwhile, the rise of newer terms reflects an innovation shift toward safer, more adaptive, and personalized learning models. GenAI not only supports administrative tasks but also facilitates deeper and more meaningful learning interactions. The study underscores the strategic importance of integrating GenAI into curricula and fostering AI literacy among teachers and students to promote responsible and effective adoption. These insights pave the way for future research and practical implementation of intelligent learning systems in educational environments.

Keywords


Artificial intelligence techno; Artificial intelligence; Adversarial learning; Federated learning; Contrastive learning

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References


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DOI: https://doi.org/10.59247/jtped.v1i2.11

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