<![CDATA[This study examines the optimization of educational evaluation through the integration of real-time feedback and artificial intelligence (AI). A descriptive-analytical literature review was conducted by analyzing recent and relevant scholarly publications. The novelty of this study lies in proposing a balanced approach that integrates AI-driven automated evaluation with the pedagogical role of educators in delivering meaningful feedback. The findings indicate that AI enables adaptive, personalized, and real-time feedback, enhancing student engagement and learning responsiveness. However, its effectiveness depends on integration with human-centered pedagogical practices that promote reflection, critical thinking, and adaptive learning. The study further demonstrates that feedback quality, students’ adaptive learning capacity, and learning outcomes are dynamically interconnected and mutually reinforcing. Based on these findings, the study proposes a conceptual framework for balancing AI-based assessment with human feedback in educational evaluation. The framework highlights the complementary roles of technological efficiency and pedagogical judgment in supporting meaningful learning. Practically, the findings underscore the importance of adopting blended assessment approaches and strengthening educators’ assessment literacy. Future research should empirically validate the proposed framework across diverse educational contexts and authentic learning environments.]]>
