A Review of Neuro-Symbolic, Multi-Modal Intelligent Tutoring Systems for Advancing Adaptive and Personalized Learning

This review examines how the intersection of adaptive learning with neuro-symbolic, multi-modal intelligent tutoring systems optimizes learning experiences. By combining symbolic reasoning with neural networks, these systems use heterogeneous data (e.g., textual, visual, auditory) to tailor lesson content and pace, improving learner engagement and outcomes. We highlight the particular significance o f these systems for Uzbekistan’s educational enhancement, outlining the country’s adoption potential based on its infrastructure and local context. Finally, we discuss outstanding research questions and implementation models for achieving local and international objectives. A comparison of recent studies shows high performance, with neuro-symbolic tutors reaching 99.5 percent unit-level accuracy, emotion-aware systems attaining 97 percent accuracy on MELD, and adaptive platforms increasing pass rates by up to 28 percentage points.

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