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Mofu, Alfred Michel
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Religion Arts Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Artificial Intelligence for Competency-Based Assessment in Vocational Education Mofu, Alfred Michel; Sari, Praramadini; Saroh, Vetin Yumita; Fajaryati, Nuryake; Utami, Pipit; Sahria, Yoga
Journal of Research in Social Science and Humanities Vol 5, No 4 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i4.564

Abstract

The 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.
Co-Authors Nuryake Fajaryati Pipit Utami Sari, Praramadini Saroh, Vetin Yumita Yoga Sahria