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All Journal Journal of Research in Social Science and Humanities Advance Sustainable Science, Engineering and Technology (ASSET)
Yoga Sahria
Universitas Negeri Yogyakarta
Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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AI-Supported Practical Learning in Vocational Education: Challenges and Design Principles Yunda Michel Rismawati; Nunung Setiawati; Erik Yumita Sudharta; Putu Sudira Fajaryati; Pipit Utami; Yoga Sahria
Journal of Research in Social Science and Humanities Vol 5, No 3 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i4.565

Abstract

Artificial intelligence (AI) is increasingly integrated into vocational education to support practical skill development and technology-enhanced training environments. However, existing studies remain fragmented across different technological applications and provide limited conceptual understanding of how AI technologies collectively support practical learning processes. This study conducts a systematic literature review following the PRISMA 2020 guideline to synthesize current evidence on AI-supported practical learning in vocational education. Seventeen studies published between 2018 and 2025 were identified from the Scopus database and analyzed through thematic synthesis. The findings indicate that AI technologies are commonly implemented through simulation platforms, intelligent tutoring systems, learning analytics and performance monitoring tools, adaptive learning systems, and AI-supported experiential learning environments. Five recurring pedagogical mechanisms were identified: simulation-based practice, intelligent skill guidance, performance feedback and analytics, adaptive learning pathways, and experiential or work-based learning. The review also highlights implementation challenges related to infrastructure, data availability, ethical concerns, and teacher AI literacy. Based on these findings, a conceptual framework is proposed to explain how AI technologies support practical learning and competency development in vocational education. The synthesis also suggests opportunities for integrating emerging approaches such as multimodal learning analytics and facial expression recognition (FER) to better understand learner engagement during practical training activities.
Camera-Based Smart Mirror with Machine Learning for Postural Analysis: System Development and Reliability Evaluation Fitri Yani; Yoga Sahria; Siti Nadhir Ollin Norlinta; Riska Risty Wardhani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3097

Abstract

Early postural assessment using camera-based systems remains technically challenging due to variability in user positioning and limited evaluation of measurement repeatability. This study presents the development and repeatability evaluation of a smart mirror system for automated postural analysis using pretrained pose estimation and rule-based geometric classification. The system consists of a fixed camera mounted above a mirror and a connected computing device for real-time processing and visual feedback. Anatomical landmarks were detected from standardized anterior, posterior, and lateral views using an AI-based pose estimation model, and postural asymmetry was quantified using bilateral distance ratios and angular deviation thresholds derived from literature. Reliability was evaluated through repeated measurements to assess the consistency of landmark detection and postural classification outputs. Forty adolescents (age 12.8 ± 0.56 years; 28 males, 12 females) participated in present study. The system intra-rater reliability was evaluated by calculating Intraclass Correlation Coefficients (ICC) for the landmark data and Cohen's Kappa for posture classifications. The system demonstrated excellent reliability for key landmarks in scapula (ICC = 0.98, 95%CI 0.97-0.99) and hip-knee-ankle (ICC = 0.98, 95%CI 0.98-0.99). The classifications for scoliosis assessment also showed excellent agreement (κ = 0.90). These results indicate that the proposed system can produce repeatable posture measurements under controlled conditions; however, this study evaluates repeatability only and does not assess diagnostic accuracy or clinical validity. Further validation against clinical reference standards is required before broader application. 
Co-Authors Erik Yumita Sudharta Fitri Yani Nunung Setiawati Pipit Utami Putu Sudira Fajaryati Siti Nadhir Ollin Norlinta wardhani, Riska risty Yunda Michel Rismawati