Modeling Vocational Preferences in STEM Students Through Explainable and Fuzzy AI to Support Personalized Learning

Abstract

Understanding students’ vocational preferences in STEM domains is a complex challenge characterized by uncertainty, subjectivity, and overlapping interests. Traditional profiling approaches often rely on rigid categorizations that fail to capture the hybrid and dynamic nature of learners. This study proposes FAS-XAI, a reproducible learning analytics framework that integrates fuzzy logic and explainable artificial intelligence for interpretable profiling of STEM vocational preferences. The methodology combines fuzzy AHP for criterion weighting, Fuzzy C-Means clustering to identify overlapping profiles, and XGBoost for supervised validation, complemented by SHAP and LIME to provide global and local explanations of model behavior. The study is framed as a methodological simulation under controlled conditions, using synthetic data to evaluate the internal coherence, transparency, and transferability of the proposed pipeline. The results show that the framework can generate multidimensional and interpretable learner profiles, with resilience, communication, and commitment emerging as relevant discriminative dimensions within the simulated setting. Overall, the proposed approach provides a reproducible methodological basis for future empirical applications in personalized learning, vocational guidance, and AI-supported educational decision-making.