Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015–2025)
DOI:
https://doi.org/10.33394/jp.v13i2.19158Keywords:
Deep Learning, Education, Meaningful Principles, Bibliometric AnalysisAbstract
This study aims to explore how Deep Learning (DL) contributes to meaningful learning in response to the increasing demand for ethical, transparent, and student-centered applications of Artificial Intelligence (AI) in education. The study employs a bibliometric analysis of 110 Scopus-indexed publications published between 2015 and 2025, using Biblioshiny in the R Bibliometrix package to identify research trends, key contributors, institutional productivity, and thematic developments. The analysis encompasses publication trends, citation patterns, author and country productivity, collaboration networks, and keyword co-occurrence. The findings indicate that, although the majority of studies originate from computer science and engineering, there has been a growing shift toward education and the social sciences, reflecting an increasingly interdisciplinary orientation, particularly after 2020. Emerging themes such as explainable AI, adaptive learning, and ethical AI suggest a transition from technology-driven innovation toward pedagogy-oriented and ethically grounded practices. Keyword co-occurrence analysis reveals three dominant thematic clusters: (1) explainable AI in pedagogy, (2) adaptive learning systems, and (3) ethical and human-centered AI in education. This shift reflects a broader movement toward human-centered AI that enhances learning relevance, personalization, and engagement. Overall, the integration of DL in education is evolving beyond technical efficiency to support meaningful, ethical, and learner-centered educational experiences.
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