Semantic Graphs of Learning Activities from LLM Embeddings: A Lightweight and Explainable Approach for Smart Learning Systems

Smart learning systems are designed to analyze the context, needs, and progress of each student. These are becoming increasingly common, but they present challenges, such as predicting student performance and automatically managing learning activities. In this context, Large Language Models (LLMs) can be useful, as they are capable of understanding word relationships and analyzing their context. They are often associated with chatbots, which are computationally expensive, thereby complicating their integration. Instead, in this work, we propose to leverage the capabilities of LLMs through a semantic graph of activities created from sentence embeddings. This representation is a lightweight and explainable alternative. On the one hand, it requires a lower computational cost. On the other hand, it allows us to observe which activities are most similar directly. On this basis, we propose two problems to validate our proposal. In the first, we use the graph to classify new activities. In the second, we extend this representation with the temporal dimension to formulate a spatio-temporal problem and predict student performance. The results show that the semantic graph not only provides an accurate representation for the organization and classification of activities, but also offers practical advantages and improves explainability.