Understanding the Integration of Deep Learning and Artificial Intelligence in Quranic Education and Research through Bibliometric Analysis

Background/purpose. Recent developments in information and communication technology have opened up many new opportunities in various research fields, including religious studies (Burton, 2020). One of the most notable innovations was integrating artificial intelligence and deep learning in religious studies, including Quranic education and research. This study conducted a bibliometric analysis of Quranic education and research integrating deep learning and artificial intelligence. The study evaluated five key indicators in bibliometrics: scientific production, authors, country level, affiliations, and sources or journals.

Materials/methods. Data were collected from the Scopus database using Boolean search, and the final data set included 244 studies published between 1966 and 2024. Data were analyzed using R/R-Studio, VOSViewer, and Microsoft Excel software.

Results. The results showed that most publications were from the United States, the United Kingdom, and China, with the University of Michigan and Ann Arbor as the most productive institutions. The analysis also revealed that publications integrating DL and AI in Quranic research peaked in 2023. Network visualization identified three main clusters that illustrate the relationship between terms and concepts, with keywords such as "religion" and "Artificial Intelligence" being the most frequently discussed topics.

Conclusion. The study provides significant methodological insights for researchers interested in integrating AI and DL in religious research and encourages further research.

Keywords: Deep Learning, Artificial Intelligence, Religions, Quranic, Bibliometric

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