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All Journal Bincang Sains dan Teknologi Jurnal Genesis Indonesia
Arslan, Aysel
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Social Sciences Transportation

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Artificial Intelligence in Manufacturing: A Perspective on Productivity Gains and Labor Displacement Arslan, Aysel; Silitonga, Roland Y. H.
Bincang Sains dan Teknologi Vol. 5 No. 01 (2026): Bincang Sains dan Teknologi
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/bst.v5i01.2171

Abstract

The integration of Artificial Intelligence (AI) into manufacturing has become a key driver of industrial transformation in the era of Industry 4.0, offering substantial gains in efficiency, productivity, and operational performance. However, its implications for human labor remain a critical concern. This study aims to examine the dual impact of AI adoption in manufacturing, focusing on both technological benefits and socio-economic consequences, particularly labor displacement, job transformation, and workforce sustainability. This research employs a systematic literature review of interdisciplinary studies published between 2010 and 2024, using thematic synthesis to analyze three key dimensions: labor displacement as a structural risk, the limitations of job transformation, and the emergence of human-centered AI. The findings reveal that AI disproportionately affects routine and mid-skilled jobs, contributing to labor market polarization and increasing risks of structural unemployment. While new high-skill roles emerge, their limited accessibility constrains workforce transition. The study highlights the need for a human-centered approach that integrates technological advancement with reskilling initiatives, labor protections, and inclusive policies. It contributes by providing a structured synthesis that bridges efficiency-driven and labor-oriented perspectives in AI-driven manufacturing.
Intelligent Tutoring Systems and the Future of Problem-Solving Education: An Opinionated Synthesis Sulisworo, Dwi; Arslan, Aysel
Jurnal Genesis Indonesia Vol. 4 No. 03 (2025): Jurnal Genesis Indonesia
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.jgi.001802

Abstract

The rapid evolution of Intelligent Tutoring Systems (ITS) has transformed contemporary educational discourse, particularly regarding how automated systems can enhance students’ problem-solving abilities in STEM fields. Although empirical studies consistently show that ITS can outperform traditional instruction, the mechanisms driving these gains remain insufficiently emphasized in policy and pedagogical narratives. This article argues that ITS effectiveness stems from a synergy of seven cognitive mechanisms: adaptive scaffolding, immediate and elaborated feedback, model-tracing and knowledge-tracing, metacognitive prompting, productive practice, error-driven learning, and cognitive load regulation. An opinion-based synthesis supported by extensive literature is presented to advocate for the strategic integration of ITS within national instructional reforms. The conceptual framework includes a visual diagram representing the mechanisms and their interactions. Results and discussion are organized into three subsections The article concludes with recommendations for adopting ITS in large-scale educational systems to improve learners' higher-order problem-solving.
Co-Authors Silitonga, Roland Y. H.