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Integrating AI in teaching factories to advance Indonesian vocational schools

Jurnal Pendidikan Vokasi · Vol. 16 No. 2 (2026) · hlm. 217–228

DOI
10.21831/jpv.v16i2.91160
ISSN
2476-9401, 2088-2866
Terbit
2026-07-31
Penerbit
ADGVI & Graduate School of Universitas Negeri Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study aims to develop and evaluate an Artificial Intelligence (AI)-based teaching factory model to enhance students’ vocational competencies in alignment with industry standards. It addresses a gap in the existing literature, where teaching factory models and AI applications in education have largely been investigated separately, with limited empirically validated frameworks integrating AI to support adaptive learning and real-time performance assessment in vocational education. In this study, an AI-based teaching factory is operationally defined as a learning environment that utilizes AI to provide personalized instruction, real-time performance analytics, and industry-simulated production workflows. A mixed-methods research approach was employed, comprising needs analysis, system design and development, field trials, and effectiveness evaluation. Instrument validity was assessed using Aiken’s V, while reliability was evaluated using Cronbach’s alpha. Qualitative data collected through interviews, observations, and document analysis were analyzed thematically to identify patterns related to learning outcomes, implementation processes, and operational challenges. The study was conducted at SMKN 1 Pacet Cianjur and SMKN 11 Bandung in West Java Province, Indonesia. The findings indicate that the proposed AI-based teaching factory model significantly enhances students’ problem-solving skills, critical thinking, and readiness for technology-driven workplaces. This study contributes a validated and replicable implementation framework for integrating AI into vocational teaching factories and provides practical recommendations for teacher capacity building, curriculum alignment, and infrastructure development to support sustainable, industry-relevant vocational education.

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  • Universitas Muhammadiyah Prof Dr Hamka
  • Universitas Halu Oleo
  • State University of Semarang
  • Binus University
  • National Research and Innovation Agency
  • Universitas Indraprasta PGRI
  • Universitas Islam Negeri Raden Intan Lampung
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Ionia VeritawatiPancasila University3
Evi SopandiNational Research and Innovation Agency3
Tatang SubagyoNational Research and Innovation Agency3
Wati SukmawatiUniversitas Muhammadiyah Prof Dr Hamka2
Via NoorlatipahUniversitas Muhammadiyah Prof Dr Hamka2
Yingdong LiuUniversité de Strasbourg2
Gamal Abdel NasserUniversitas Halu Oleo1
Suriana SurianaUniversitas Halu Oleo1
Tjandra BuanaUniversitas Halu Oleo1
Sukmawati AbdullahUniversitas Halu Oleo1
Ima Astuty WunawarsihUniversitas Halu Oleo1
Nur Isiyana WiantiUniversitas Halu Oleo1
Tri Joko RaharjoState University of Semarang1
Harianingsih HarianingsihState University of Semarang1
Wiwik HidayatiAfiliasi tidak diketahui1
Bagus GinanjarAfiliasi tidak diketahui1
Atik YuliyaniAfiliasi tidak diketahui1
Tudrika AkhiliaAfiliasi tidak diketahui1
Alamsyah AlamsyahUniversitas Islam Negeri Raden Intan Lampung1
Samsudi SamsudiState University of Semarang1
Rasdi EkosiswoyoState University of Semarang1
Noerlina NoerlinaBinus University1
Yuliana YoswaraBinus University1
Somariah FitrianiUniversitas Muhammadiyah Prof Dr Hamka1
Ahmad DiponegoroUniversitas Muhammadiyah Prof Dr Hamka1
Siti LatifahJenderal Soedirman University1
Elin MarlinaUniversitas Muhammadiyah Prof Dr Hamka1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://journal.uny.ac.id/index.php/jpv/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Integrating AI in teaching factories to advance Indonesian vocational schools</dc:title>
	<dc:creator>Wahjusaputri, Sintha</dc:creator>
	<dc:creator>Nastiti, Tashia Indah</dc:creator>
	<dc:creator>Bunyamin, Bunyamin</dc:creator>
	<dc:creator>Johan, Johan</dc:creator>
	<dc:subject xml:lang="en-US">artificial intelligence</dc:subject>
	<dc:subject xml:lang="en-US">critical thinking</dc:subject>
	<dc:subject xml:lang="en-US">curriculum</dc:subject>
	<dc:subject xml:lang="en-US">learning model</dc:subject>
	<dc:subject xml:lang="en-US">teaching factory</dc:subject>
	<dc:description xml:lang="en-US">This study aims to develop and evaluate an Artificial Intelligence (AI)-based teaching factory model to enhance students’ vocational competencies in alignment with industry standards. It addresses a gap in the existing literature, where teaching factory models and AI applications in education have largely been investigated separately, with limited empirically validated frameworks integrating AI to support adaptive learning and real-time performance assessment in vocational education. In this study, an AI-based teaching factory is operationally defined as a learning environment that utilizes AI to provide personalized instruction, real-time performance analytics, and industry-simulated production workflows. A mixed-methods research approach was employed, comprising needs analysis, system design and development, field trials, and effectiveness evaluation. Instrument validity was assessed using Aiken’s V, while reliability was evaluated using Cronbach’s alpha. Qualitative data collected through interviews, observations, and document analysis were analyzed thematically to identify patterns related to learning outcomes, implementation processes, and operational challenges. The study was conducted at SMKN 1 Pacet Cianjur and SMKN 11 Bandung in West Java Province, Indonesia. The findings indicate that the proposed AI-based teaching factory model significantly enhances students’ problem-solving skills, critical thinking, and readiness for technology-driven workplaces. This study contributes a validated and replicable implementation framework for integrating AI into vocational teaching factories and provides practical recommendations for teacher capacity building, curriculum alignment, and infrastructure development to support sustainable, industry-relevant vocational education.</dc:description>
	<dc:publisher xml:lang="en-US">ADGVI &amp; Graduate School of Universitas Negeri Yogyakarta</dc:publisher>
	<dc:date>2026-07-31</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-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.uny.ac.id/index.php/jpv/article/view/91160</dc:identifier>
	<dc:identifier>10.21831/jpv.v16i2.91160</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Pendidikan Vokasi; Vol. 16 No. 2 (2026); 217-228</dc:source>
	<dc:source>2476-9401</dc:source>
	<dc:source>2088-2866</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.uny.ac.id/index.php/jpv/article/view/91160/26135</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Sintha Wahjusaputri, Tashia Indah Nastiti, Bunyamin Bunyamin, Johan Johan</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>