A Critical Study of Equity and Inclusion in Machine Learning Platforms: Algorithmic Bias as a New Educational Barrier
DOI:
https://doi.org/10.5281/Keywords:
machine learning, educational equityAbstract
The integration of artificial intelligence in education promises personalization an defficiency, but introduces risks of perpetuating inequalities. Algorithmic bias acts as a barrier to access and retention that operates at the code level, affecting educational rights across all academic levels. This systematic review examines how biases in data and algorithmic design generate discrimination based on gender, socioeconomic status, and cultural background. Following the PRISMA guidelines, 33 studies published between 2018 and 2025 were analyzed. Three categories of bias were identified: historical data bias, representational bias, and validation bias. Predictive platforms (dropout, recommendation, automated assessment) exhibit systematic disparities among demographic groups. This review has limitations: the methodological heterogeneity of the included studies. Mitigation strategies include pre-processing, in-processing, and post-processing interventions, with varying effectiveness. The proposed ethical framework offers a roadmap for designing truly participatory AI systems. As an original contribution, an integrative ethical framework for the development of inclusive AI in education is proposed, based on five principles: distributive justice, radical transparency, student agency, cognitive diversity, and participatory governance. It is concluded that algorithmic equity requires a socio-technical approach that involves educators, communities, and designers in the co-construction of inclusive systems.
Keywords: machine learning; educational equity; artificial intelligence; social justice; algorithmic bias
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