Artificial Intelligence dalam Assessment Pendidikan: A Systematic Literature Review tentang Model, Validitas, dan Implikasi Evaluatif

Yuni Susilowati

Abstract


This study aims to identify and classify Artificial Intelligence (AI) models applied in educational assessment, analyze how validity and reliability aspects are examined in previous studies, and evaluate the evaluative and ethical implications of AI application. The research method used a qualitative approach with a Systematic Literature Review (SLR) design, which examined literature from the Scopus, DOAJ, and Google Scholar databases, covering publications from the last 10 years (2016–2025). The selection process was carried out systematically based on inclusion and exclusion criteria, followed by data extraction and analysis using thematic analysis techniques. The results of the study show an increasing trend in the use of AI in educational assessment, particularly in automated scoring, predictive analytics, and adaptive testing, with a focus on improving efficiency, personalizing evaluation, and adaptive feedback. The study also found that construct validity and score reliability remain major challenges, while evaluative and ethical implications, including algorithm transparency and student data protection, are important concerns. This literature synthesis provides a comprehensive understanding of the development of AI models, methodological challenges, and recommendations for practices and policies to optimize the use of AI in accountable, valid, and fair educational evaluation.

Keywords


Artificial Intelligence, Educational Assessment, Validity, Reliability, Ethical Implications, Systematic Literature Review.

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References


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