Transformasi Evaluasi Pembelajaran Berbasis Artificial Intelligence Pada Sekolah Menengah Kejuruan

Abstract

TRANSFORMATION OF ARTIFICIAL INTELLIGENCE-BASED LEARNING EVALUATION IN VOCATIONAL HIGH SCHOOLS

The digital transformation in the education sector has driven the adoption of Artificial Intelligence (AI) as an increasingly relevant learning evaluation instrument. This study comprehensively analyzes the transformation of AI-based learning evaluation in Vocational High Schools (SMK) in Indonesia, covering implementation dimensions, structural challenges, and its impact on the quality of student competency assessment. The method used is a Systematic Literature Review (SLR) with PRISMA protocol, analyzing 45 scientific articles published in the period 2022–2026 from the Scopus, Web of Science, and Google Scholar databases. The selection process was carried out through three filtering stages producing high-quality articles with a minimum QUALIS score of B2. The results show that AI implementation in learning evaluation at SMK provides assessment efficiency of up to 68%, improves student competency identification accuracy by 54%, facilitates more personalized and real-time feedback, and reduces subjective assessment bias by 43%. Thematic analysis reveals six main implementation patterns: auto-grading (78%), learning pattern analysis (65%), adaptive material recommendations (61%), digital portfolio assessment (57%), academic dishonesty detection (52%), and learning outcome prediction (43%). Key challenges include limited technology infrastructure (72%), lack of teacher digital competency (65%), student data privacy issues (48%), school cultural resistance to change (39%), and absence of specific technical regulations (34%). This study recommends developing an adaptive and contextual AI ecosystem, accompanied by strengthening digital literacy for educators and establishing comprehensive student data governance policies.