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Deep Learning as Artificial Intelligence Technology in Physical Education: A Literature Review Based on Bloom's Taxonomy

Journal of Physical Education Health and Sport · Vol. 13 No. 1 (2026) · hlm. 72–80

DOI
10.15294/jpehs.v13i1.43856
ISSN
2354-7901, 2354-8231
Terbit
2026-04-30
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Technological advancements necessitate a shift in education, especially in physical education, which sometimes remains confined to gross motor skills without enhancing cognitive processes. This study seeks to analyze and assess the role of Deep Learning (DL) technology within Artificial Intelligence (AI) in enhancing the quality of physical education instruction based on Bloom's Taxonomy framework. The method used is a Systematic Literature Review (SLR) following the PRISMA standards. Data were collected through the Google Scholar, Scopus, and Elsevier databases using the Publish or Perish tool, which produced 10 selected articles to be synthesized after undergoing a rigorous selection process. The study's results indicate that the incorporation of Deep Learning can elevate learning from simple imitation of movement to profound analytical comprehension. The use of algorithms such as ResNet-101 and 3D pose estimation enables precise biomechanical analysis and objective performance evaluation that supports higher-order thinking skills (HOTS). In conclusion, this technology acts as a catalyst in achieving physical literacy and student learning independence. The integration of AI with student-centered pedagogical strategies is the key to the future of holistic physical education.

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Rizal Ahmad FauziAfiliasi tidak diketahui6
Aam Ali RahmanAfiliasi tidak diketahui3
Ayi SuhermanAfiliasi tidak diketahui2
Muhammad Agil Nur RiyanAfiliasi tidak diketahui2
Adang SudrazatAfiliasi tidak diketahui2
Bintang Aura DewiAfiliasi tidak diketahui2
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Harum Mita MaharaniAfiliasi tidak diketahui1
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Dinar DinangsitAfiliasi tidak diketahui1
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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jpehs/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Deep Learning as Artificial Intelligence Technology in Physical Education: A Literature Review Based on Bloom's Taxonomy</dc:title>
	<dc:creator xml:lang="en">Rubby Syihabuddin</dc:creator>
	<dc:creator xml:lang="en">Indra Safari</dc:creator>
	<dc:creator xml:lang="en">Dewi Susilawati</dc:creator>
	<dc:subject xml:lang="en">deep learning model</dc:subject>
	<dc:subject xml:lang="en">Physical Education</dc:subject>
	<dc:subject xml:lang="en">Artificial Intelligence </dc:subject>
	<dc:subject xml:lang="en">Bloom's Taxonomy</dc:subject>
	<dc:description xml:lang="en">Technological advancements necessitate a shift in education, especially in physical education, which sometimes remains confined to gross motor skills without enhancing cognitive processes. This study seeks to analyze and assess the role of Deep Learning (DL) technology within Artificial Intelligence (AI) in enhancing the quality of physical education instruction based on Bloom's Taxonomy framework. The method used is a Systematic Literature Review (SLR) following the PRISMA standards. Data were collected through the Google Scholar, Scopus, and Elsevier databases using the Publish or Perish tool, which produced 10 selected articles to be synthesized after undergoing a rigorous selection process. The study's results indicate that the incorporation of Deep Learning can elevate learning from simple imitation of movement to profound analytical comprehension. The use of algorithms such as ResNet-101 and 3D pose estimation enables precise biomechanical analysis and objective performance evaluation that supports higher-order thinking skills (HOTS). In conclusion, this technology acts as a catalyst in achieving physical literacy and student learning independence. The integration of AI with student-centered pedagogical strategies is the key to the future of holistic physical education.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jpehs/article/view/43856</dc:identifier>
	<dc:identifier>10.15294/jpehs.v13i1.43856</dc:identifier>
	<dc:source xml:lang="en">Journal of Physical Education Health and Sport; Vol. 13 No. 1 (2026); 72 - 80</dc:source>
	<dc:source>2354-7901</dc:source>
	<dc:source>2354-8231</dc:source>
	<dc:source>10.15294/jpehs.v13i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jpehs/article/view/43856/8608</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Journal of Physical Education Health and Sport</dc:rights>
</oai_dc:dc>