{
      "volume": 13,
      "title": "Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015\u20132025)",
      "lastmod": "2026-07-17 02:02:27",
      "number": 2,
      "refereed": "TRUE",
      "datestamp": "2026-07-17 02:02:27",
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      "rev_number": 6,
      "official_url": "https://e-journal.undikma.ac.id/index.php/pedagogy/article/view/19158",
      "creators": [
        {
          "name": {
            "honourific": null,
            "given": "Nurul Mawadah",
            "family": "Salsabila",
            "lineage": null
          },
          "id": null
        },
        {
          "name": {
            "honourific": null,
            "given": "lismawati",
            "family": "lismawati",
            "lineage": null
          },
          "id": "lismawati@uhamka.ac.id"
        }
      ],
      "uri": "http://repository.uhamka.ac.id/id/eprint/53009",
      "ispublished": "pub",
      "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.",
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            "rev_number": 2,
            "language": "en",
            "uri": "http://repository.uhamka.ac.id/id/document/247071"
          }
      ],
      "date_type": "published",
      "status_changed": "2026-07-17 02:02:27",
      "full_text_status": "public",
      "type": "article",
      "subjects": [
        "A"
      ],
      "eprint_status": "archive",
      "date": "2026-04-20",
      "publication": "Mapping the Landscape of Deep Learning in Meaningful Principles: A Decade-Long Bibliometric Review (2015\u20132025)"
    }