@prefix dc: .
@prefix xsd: .
@prefix epid: .
@prefix geo: .
@prefix void: .
@prefix owl: .
@prefix dct: .
@prefix rdfs: .
@prefix foaf: .
@prefix eprel: .
@prefix cc: .
@prefix skos: .
@prefix event: .
@prefix ep: .
@prefix bibo: .
@prefix rdf: .
@prefix doi: .
<>
foaf:primaryTopic ;
rdfs:comment "The repository administrator has not yet configured an RDF license."^^xsd:string .
dct:hasPart ;
ep:hasFile ;
rdf:type bibo:Document,
ep:Document;
rdfs:label "Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015–2025) (Text)"^^xsd:string .
rdf:_1 ;
rdf:_2 .
rdfs:label "Nurul Salsabila.pdf"^^xsd:string .
dc:format "text/html";
dc:title "HTML Summary of #53009 \n\nMapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015–2025)\n\n";
foaf:primaryTopic .
bibo: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 movementtoward 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."^^xsd:string;
bibo:authorList ;
bibo:issue "2";
bibo:status ,
;
bibo:volume "13";
dc:hasVersion ;
dct:creator ,
;
dct:date "2026-04-20";
dct:isPartOf ,
;
dct:subject ;
dct:title "Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015–2025)"^^xsd:string;
ep:hasDocument ;
ep:hasPublished ;
rdf:type bibo:AcademicArticle,
bibo:Article,
ep:ArticleEPrint,
ep:EPrint;
rdfs:seeAlso .
rdf:type skos:Concept;
skos:prefLabel "B Philosophy. Psychology. Religion"@en .
foaf:familyName "Salsabila"^^xsd:string;
foaf:givenName "Nurul Mawadah"^^xsd:string;
foaf:name "Nurul Mawadah Salsabila"^^xsd:string;
rdf:type foaf:Person .
foaf:familyName "lismawati"^^xsd:string;
foaf:givenName "lismawati"^^xsd:string;
foaf:name "lismawati lismawati"^^xsd:string;
rdf:type foaf:Person .
foaf:name "Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015–2025)"^^xsd:string;
rdf:type bibo:Collection .