(Universitas Negeri Yogyakarta, Indonesia)(2) Praramadini Sari
(Universitas Negeri Yogyakarta, Indonesia)(3) Vetin Yumita Saroh
(Universitas Negeri Yogyakarta, Indonesia)(4) Nuryake Fajaryati
(Universitas Negeri Yogyakarta, Indonesia)(5) Pipit Utami
(Universitas Negeri Yogyakarta, Indonesia)(6) Yoga Sahria
(Universitas Negeri Yogyakarta, Indonesia)*corresponding author
AbstractThe rapid adoption of artificial intelligence in education has increasingly influenced research in technical and vocational education and training (TVET). However, much of the existing literature focuses primarily on prediction-oriented learning analytics rather than on competency-based assessment frameworks that are central to vocational education. This study investigates how artificial intelligence has been applied within vocational education research and examines the extent to which competency-based assessment principles are represented in literature. A systematic literature review was conducted using the PRISMA protocol, combined with layered bibliometric mapping using VOSviewer to explore structural and conceptual patterns in the research field. The dataset was constructed from Scopus-indexed journal articles published between 2020 and 2025. Bibliometric results indicate that machine learning, deep learning, and educational data mining dominate the research landscape, while competency constructs remain relatively peripheral. The thematic synthesis further reveals limited attention to authentic performance modeling and explainable artificial intelligence within assessment contexts. In response to these gaps, the study proposes a conceptual framework for AI-supported competency-based assessment in vocational education that integrates construct-grounded modeling, authentic performance analytics, and explainable decision architectures. The framework provides a conceptual foundation for aligning artificial intelligence technologies with competency-oriented evaluation in vocational learning environments.
KeywordsArtificial Intelligence, Competency-Based Assessment, Vocational Education, Explainable AI, Learning Analytics
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DOIhttps://doi.org/10.47679/jrssh.v5i4.564 |
Article metrics10.47679/jrssh.v5i4.564 Abstract views : 59 |
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Copyright (c) 2026 Alfred Michel Mofu, Praramadini Sari, Vetin Yumita Saroh, Nuryake Fajaryati, Pipit Utami, Yoga Sahria
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