A Neuro-Symbolic, Multi-Modal Architecture for Advancing Adaptive Intelligent Tutoring Systems

The digital transformation of education has under-scored the demand for truly personalized learning environments. However, current Intelligent Tutoring Systems (ITS) often face a dichotomy: symbolic, rule-based systems offer pedagogical interpretability but are brittle, while purely connectionist models excel at pattern recognition but operate as "black boxes" lacking deep pedagogical reasoning. This paper addresses this critical gap by proposing a novel hybrid architecture for a neuro-symbolic, multi-modal ITS. The proposed system synergizes a symbolic pedagogical knowledge graph with a suite of deep learning models designed to process multi-modal student data streams, including textual input, vocal prosody, and facial expressions. The core hypothesis is that this integration allows for a more holistic and accurate real-time assessment of a student’s cognitive and affective state, leading to more effective, timely, and pedagogically-sound interventions. We present the theoretical framework of this architecture, detailing the mechanisms for multi-modal fusion and neuro-symbolic interaction. A comparative analysis suggests that this approach holds significant potential to surpass the limitations of existing ITS paradigms, thereby creating more engaging and effective personalized learning experiences. The practical significance lies in its potential to deliver scalable, nuanced, and human-like tutoring across various educational domains. We also report preliminary component-level pilot results: a lightweight 3-block CNN for four-class affect detection on a subset mapped from FER2013 shows accuracy rising from 43.8% at 10 epochs to 56.5% at 150 epochs (macro-F1 from 0.35 to 0.51). A TF-IDF + Logistic Regression baseline for text-based confusion signals reaches 91.7% accuracy on a 60-item synthetic set. These sanity checks are not intended as state-of-the-art, but they demonstrate feasibility and motivate our staged validation plan.

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