Marín Díaz, Gabriel2026-06-122026-06-122026Marín Díaz, G. (2026). Modeling vocational preferences in stem students through explainable and fuzzy ai to support personalized learning. Education Sciences, 16(6), 917. https://doi.org/10.3390/educsci160609172227-7102https://hdl.handle.net/11268/17160Understanding 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.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Computación y tecnologíajournal article10.3390/educsci16060917open accessInteligencia artificialAnálisis de datosLógica matemáticaGoal 4: Quality educationGoal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovationGoal 10: Reduce inequality within and among countries