Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015–2025)

Abstract

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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