<![CDATA[The rapid integration of artificial intelligence into educational assessment has opened new avenues for evaluating independent learning processes. This article examines the key directions for employing AI in the assessment methodology of self-directed learning, synthesizing recent empirical evidence and theoretical frameworks. The analysis identifies four primary assessment trajectories: automated formative feedback, intelligent tutoring systems, adaptive analytics dashboards, and performance-based authentic assessment redesign. Drawing on meta-analytic findings from 32 empirical studies and systematic reviews, the article demonstrates that AI-based interventions significantly enhance cognitive and metacognitive regulation, while effects on behavioral regulation remain variable. The findings indicate that conversational and interactive AI systems yield the broadest benefits for self-directed learning, whereas supportive and analytical tools strengthen specific regulatory subprocesses. The article concludes with recommendations for educators and policymakers on aligning AI design with pedagogical goals to ensure that AI functions as an empowering scaffold rather than a substitute for learner autonomy.]]>
Copyrights © 2026
