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Mapping Deep Learning Quality in Senior High Schools Adopting the Deep Learning, Coding & Artificial Intelligence (DLCAAI) Model in Central Java as a Foundation for Evidence-Based Educational Policy

Forum Ilmu Sosial · Vol. 53 No. 01 (2026) · pp. 65–80

DOI
10.15294/fis.v53i01.50119
ISSN
2549-0745, 1412-971X
Published
2026-06-23
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Educational transformation in the era of artificial intelligence requires schools to build a humanistic-digital, reflective, collaborative, and data-driven learning ecosystem. This study aims to map the implementation quality of deep learning in State Senior High Schools adopting the Deep Learning, Coding & Artificial Intelligence (DLCAAI) model in Central Java and formulate its implications for evidence-based policy development (baseline assessment), involving 316 teachers from all DLCAAI model schools in the region. The research instrument was a 5-point Likert-scale questionnaire with 75 items covering deep learning principles, learning experiences,t he deep learning framework, graduate profiles, and learning assessment. Validity tests showed all items were valid (corrected item-total correlation > 0.30), while instrument reliability was very high (Cronbach’s Alpha = 0.992). Data analysis employed means, percentages, categories, and comparative dimensional analysis. Results indicate that the quality of deep learning implementation is 93.43%. The strongest dimensions include student-centered learning, psychological safety, character development, inclusivity, and digital integration. However, metacognitive reflection, formative assessment, learning differentiation, cross-disciplinary learning, and the utilization of learning data still require strengthening. The novelty of this research lies in developing the Baseline Architecture of Humanistic-Digital Deep Learning Ecosystem framework, which integrates deep learning, AI, reflective pedagogy, student agency, and evidence-based educational policy. This study confirms that baseline assessment functions as an educational diagnostic, a compass for school transformation, and a policy navigation system in the humanistic and sustainable AI-based educational transformation.

Keywords

Collaboration network of the authors

35 authors · 86 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dian Fithra PermanaAffiliation unknown3
Radini SintaAffiliation unknown3
Moh. UyubAffiliation unknown3
Dian Fithra PermanaAffiliation unknown3
Joko WidodoAffiliation unknown2
Angga Pandu WijayaAffiliation unknown2
Syakila MaulidaAffiliation unknown2
Amanda Dewi PuspitaAffiliation unknown2
Siti MursidahAffiliation unknown2
Gunawan TjokroAffiliation unknown2
Muhamad TaufiqState University of Semarang1
Zaenuri ZaenuriState University of Semarang1
Arief RohmanAffiliation unknown1
Muhammad KhafidAffiliation unknown1
Nina FarlianaAffiliation unknown1
RusdartiAffiliation unknown1
Teguh Hardi RaharjoAffiliation unknown1
Pingkan Anggia KinaryosihAffiliation unknown1
Angga WijayaAffiliation unknown1
Liza Dwi AnggraeniAffiliation unknown1
Syaefa Habsyah Nur MaulidaAffiliation unknown1
Edy SuryantoAffiliation unknown1
Muhammad Nukha MurtadloAffiliation unknown1
Angga Dwi ArdiantoAffiliation unknown1
Tutik WijayantiAffiliation unknown1
Eta Yuni LestariAffiliation unknown1
Yohana Endang TrirahayuAffiliation unknown1
Tafriyadhur Risa BillahAffiliation unknown1
Heni Agus PurwantiAffiliation unknown1
Asrori AsroriAffiliation unknown1
Fahrian Aldi AlfiansyaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/FIS/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Mapping Deep Learning Quality in Senior High Schools Adopting the Deep Learning, Coding &amp; Artificial Intelligence (DLCAAI) Model in Central Java as a Foundation for Evidence-Based Educational Policy</dc:title>
	<dc:creator xml:lang="en">Suharja</dc:creator>
	<dc:creator xml:lang="en">Fathur Rokhman</dc:creator>
	<dc:creator xml:lang="en">Eko Handoyo</dc:creator>
	<dc:creator xml:lang="en">Nina Oktarina</dc:creator>
	<dc:subject xml:lang="en">baseline assessment; deep learning; DLCAAI; humanistic-digital learning ecosystem; evidence-based educational policy; artificial intelligence.</dc:subject>
	<dc:description xml:lang="en">Educational transformation in the era of artificial intelligence requires schools to build a humanistic-digital, reflective, collaborative, and data-driven learning ecosystem. This study aims to map the implementation quality of deep learning in State Senior High Schools adopting the Deep Learning, Coding &amp;amp; Artificial Intelligence (DLCAAI) model in Central Java and formulate its implications for evidence-based policy development (baseline assessment), involving 316 teachers from all DLCAAI model schools in the region. The research instrument was a 5-point Likert-scale questionnaire with 75 items covering deep learning principles, learning experiences,t he deep learning framework, graduate profiles, and learning assessment. Validity tests showed all items were valid (corrected item-total correlation &amp;gt; 0.30), while instrument reliability was very high (Cronbach’s Alpha = 0.992). Data analysis employed means, percentages, categories, and comparative dimensional analysis. Results indicate that the quality of deep learning implementation is 93.43%. The strongest dimensions include student-centered learning, psychological safety, character development, inclusivity, and digital integration. However, metacognitive reflection, formative assessment, learning differentiation, cross-disciplinary learning, and the utilization of learning data still require strengthening. The novelty of this research lies in developing the Baseline Architecture of Humanistic-Digital Deep Learning Ecosystem framework, which integrates deep learning, AI, reflective pedagogy, student agency, and evidence-based educational policy. This study confirms that baseline assessment functions as an educational diagnostic, a compass for school transformation, and a policy navigation system in the humanistic and sustainable AI-based educational transformation. </dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-06-23</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/FIS/article/view/50119</dc:identifier>
	<dc:identifier>10.15294/fis.v53i01.50119</dc:identifier>
	<dc:source xml:lang="en">Forum Ilmu Sosial; Vol. 53 No. 01 (2026): June 2026; 65-80</dc:source>
	<dc:source>2549-0745</dc:source>
	<dc:source>1412-971X</dc:source>
	<dc:source>10.15294/fis.v50i01</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/FIS/article/view/50119/10223</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Forum Ilmu Sosial</dc:rights>
</oai_dc:dc>