Abstract This article presents a structured survey of recent approaches integrating Large Language Models (LLMs) and Knowledge Graphs (KGs) in education, with a dual focus on explainable reasoning and emotion-aware student support. The objective is to assess how neuro-symbolic architectures and affective computing enhance both transparency and learner well-being in AI-driven tutoring systems. A multimodal literature review was conducted, combining keyword-based searches across Scopus, IEEE Xplore, and SpringerLink from 2020 to 2024, with inclusion criteria focusing on studies addressing LLM explainability, KG integration, and affective adaptation in education. The selected papers were analyzed using a three-axis framework: (1) technological synergy (LLM–KG–Affective AI), (2) evaluation metrics (Pedagogical Alignment Score, Anxiety Reduction Index, Scaffolding Perplexity Divergence), and (3) equity and explainability gaps. Results reveal that hybrid systems improve interpretability, engagement, and personalization, but remain limited by the absence of metacognitive modeling and standardized affective benchmarks. Practical implications include actionable strategies for developing transparent, stress-aware, and ethically grounded tutoring systems, such as emotion-adaptive scaffolding, blockchain-based validation of explanations, and federated learning for privacy-preserving personalization.
Chaabene et al. (Tue,) studied this question.
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