<p>This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental classification algorithm, Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM), which leverages fuzzy logic to analyze student interaction data from virtual learning platforms, even when the data are only partially labeled. The proposed methodology generates human-centered explanations by extracting IF-THEN fuzzy rules from the evolving prototypes produced by DISSFCM over successive time intervals. These explanations, expressed in linguistic terms, remain accessible to non-expert stakeholders and are particularly suitable for educational contexts. The Open University Learning Analytics Dataset (OULAD) is utilized for experimentation and validation, providing a realistic scenario for semi-supervised data collection. Visual summaries of the evolving fuzzy rules support the identification of temporal patterns in streaming data. Results show that the model effectively adapts to concept drift while maintaining interpretability. Most notably, it proves robust in handling partially labeled data and variable time granularities, two challenges frequently encountered in real-world Learning Analytics scenarios. The ability to both predict student outcomes and provide intelligible explanations under such constraints highlights the practical value of the approach. To evaluate the quality and relevance of the generated explanations, an expert-based evaluation was conducted. Domain experts evaluated the clarity, usefulness, and accuracy of the explanations in terms of their support for human understanding and decision-making. The results suggest that the explanations were perceived as generally informative and useful, supporting the method’s relevance for human-centered educational applications.</p>