SAIL-Y: A Socioeconomic and Gender-Aware Career Recommender System

Abstract

This study presents SAIL-Y (Sailing Artificial Intelligence for Learning in Youth), a novel gender-focused recommender system designed to promote female participation in STEM careers through data-driven guidance. Drawing inspiration from the metaphor of an academic journey as a voyage, SAIL-Y functions as a digital compass—leveraging socioeconomic profiles and standardised test results (Saber 11, Colombia) to help students navigate career decisions in high-impact academic fields. SAIL-Y integrates multiple machine learning strategies, including collaborative filtering, bootstrapped data augmentation to rebalance gender representation, and socioeconomic-aware conditioning, to generate personalised and bias-controlled career recommendations. The system is explicitly designed to skew recommendations toward STEM disciplines for female students, countering systemic underrepresentation in these fields. Using a dataset of 332,933 Colombian students (2010–2021), we evaluate the performance of different recommendation architectures under the SAIL-Y framework. The results show that a gender-oriented recommender design increases the proportion of STEM career recommendations for female students by up to 25% compared to reference models. Beyond technical contributions, this work proposes an ethically aligned paradigm for educational recommender systems—one that empowers rather than merely predicts. SAIL-Y is thus envisioned as both a methodological tool and a socio-educational intervention, supporting more equitable academic journeys for future generations.