<![CDATA[Formative assessment plays an important role in providing continuous feedback that supports the student learning process. However, formative assessment practices in higher education often remain static and insufficiently responsive to individual learner differences. This study examines the integration of artificial intelligence (AI) into formative assessment by exploring patterns of student engagement, learning styles, and academic achievement within a data-informed learning environment. The findings indicate that student engagement is closely associated with academic performance and dropout risk, suggesting its potential function as an early indicator of academic vulnerability. Differences in learning styles are also reflected in formative performance, highlighting the importance of personalized instructional support. These results illustrate how AI-supported analysis can enhance formative assessment by enabling timely feedback, adaptive learning support, and the early identification of students at risk. Beyond confirming established relationships, this study emphasizes the conceptual role of artificial intelligence in reshaping formative assessment practices. AI is positioned as a formative assessment mediator that integrates learning analytics to support personalization, predictive insight, and adaptive feedback. This conceptualization contributes to formative assessment theory by demonstrating how data-driven intelligence can operationalize continuous, student-centered assessment in higher education. Rather than functioning merely as an analytical tool, artificial intelligence is shown to fundamentally reshape formative assessment by enabling continuous, predictive, and adaptive feedback mechanisms that are not achievable through conventional assessment approaches.]]>
Copyrights © 2026
