<![CDATA[This study analyzes the potential integration of fuzzy logic and Multi-Criteria Decision-Making (MCDM) to measure the Career Adapt-Abilities Scale (CAAS) for Mechanical Engineering vocational high school students. Rapid industrial automation and digitalization require more accurate assessments of career adaptability, while conventional methods remain limited in addressing uncertainty and subjective responses. Using a systematic literature review and bibliometric analysis of 25 Scopus-indexed articles published between 2021 and 2026, this study examines research trends in Fuzzy-AHP, Fuzzy-TOPSIS, and Fuzzy Inference Systems and their relevance to evaluating the psychological dimensions of concern, control, curiosity, and confidence. The findings indicate that fuzzy-MCDM effectively processes linguistic and multi-criteria data, enabling a more comprehensive and contextual assessment of career adaptability. The study offers a conceptual framework for improving career adaptability evaluation and supporting vocational students’ career readiness in responding to the demands of modern industry.]]>
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