Evaluasi Pembelajaran Berbasis Artificial Intelligence untuk Meningkatkan Kualitas Asesmen Peserta Didik

Abstract

The rapid development of Artificial Intelligence (AI) has transformed various aspects of education, including learning evaluation systems. Learning evaluation is no longer viewed solely as a tool for measuring students' achievement but also as an essential process for improving the quality of teaching and learning. However, conventional assessment practices still encounter several challenges, including teachers' administrative workload, limited opportunities to provide immediate feedback, and assessment systems that are not fully responsive to students' diverse learning characteristics. This study aims to analyze the implementation of Artificial Intelligence-based learning evaluation in improving the quality of student assessment. The research employed a qualitative approach using a library research design. Data were collected from scientific journal articles, academic books, conference proceedings, and educational policy documents discussing the application of Artificial Intelligence in learning evaluation. The collected data were analyzed using content analysis through the stages of data reduction, categorization, interpretation, and conclusion drawing. The findings indicate that Artificial Intelligence enhances assessment quality by supporting adaptive assessment, automating learning outcome analysis, providing immediate and personalized feedback, and generating comprehensive learning analytics to facilitate differentiated instruction. Furthermore, AI improves the objectivity, efficiency, and accuracy of assessment processes while reducing teachers' administrative burden, allowing them to focus more on instructional improvement. Nevertheless, the implementation of AI in educational assessment continues to face several challenges, including limited digital competencies among teachers, unequal technological infrastructure, and concerns regarding ethics, privacy, and data security. Therefore, successful implementation requires continuous professional development for educators, adequate digital infrastructure, and comprehensive policies that promote the responsible and ethical integration of Artificial Intelligence into learning evaluation systems.