INTEGRATING DIGITAL FORMATIVE ASSESSMENT WITH LEARNING ANALYTICS TO IMPROVE STUDENT INTEREST IN LEARNING IN THE AGE OF AI

Abstract

This study aims to analyze the integration of digital formative assessment with learning analytics as a strategic approach to improving student learning interest in the era of artificial intelligence (AI). This study is a Systematic Literature Review (SLR) using the PRISMA approach, examining 32 selected articles from databases such as Google Scholar, Semantic Scholar, ERIC, DOAJ, and Scopus, spanning the years 2013 to 2024, in both Indonesian and English. Key findings from this article indicate that the integration of digital formative assessment and learning analytics not only enhances learning effectiveness but also significantly strengthens students' intrinsic motivation and learning interest. Personalized feedback generated from learning analytics has proven effective in fostering self-confidence, active engagement, and self-directed learning awareness among students. However, this study also revealed limitations in implementation, such as low teacher data literacy, technological infrastructure constraints, and a lack of studies specifically linking analytics features to the psychological dimensions of learning interest. Therefore, further research is needed with a broader sample, diverse educational contexts, and the development of more accurate instruments to measure the impact of this integration in depth. The findings of this study are expected to contribute theoretically and practically to the development of adaptive, personalized, and data-driven educational policies in the digital age.