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

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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 aca-demic journey as a voyage, SAIL-Y functions as a digital compass—leveraging socioeco-nomic profiles and standardised test results (Saber 11, Colombia) to help students navi-gate 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 per-sonalised and bias-controlled career recommendations. The system is explicitly designed to skew recommendations toward STEM disciplines for female students, countering sys-temic underrepresentation in these fields. Using a dataset of over 300,000 Colombian stu-dents (2010–2021), we evaluate the performance of different recommendation architec-tures 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 pro-poses an ethically aligned paradigm for educational recommender systems—one that empowers rather than merely predicts. SAIL-Y is thus envisioned as both a methodologi-cal tool and a socio-educational intervention, supporting more equitable academic jour-neys for future generations.

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