The Algorithmic Glass Ceiling: Gender Bias and STEM Identity in AI-Supported Secondary School Education
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Keywords
algorithmic bias, STEM identity, AI in education, gender inequality, learning analytics
Abstract
Gender inequality in STEM education concerns more than participation rates or achievement gaps. It also concerns recognition: who is treated as a legitimate knower, problem solver, and future practitioner. As AI-driven learning systems enter secondary school STEM classrooms, existing research may underestimate algorithmic mediation, the process through which educational algorithms translate social patterns into content, feedback, and learning trajectories. This paper argues that AI-supported STEM environments can reorganize gender bias through three connected mechanisms. Representation sets expectations about who belongs in STEM. Interaction shapes the kind of support, challenge, and recognition students receive. Personalization can turn early behavioral differences into lasting learning paths. Drawing on research in educational AI, learning analytics, and STEM identity, this paper shifts the focus from detecting bias to understanding how students experience it. The main claim is that AI systems can make structural inequality feel like an individual difference. That matters because the effects reach beyond grades. They shape whether girls see themselves as people who can question, create, and contribute to STEM knowledge. The paper contributes a framework for understanding algorithmic bias as a process that forms identity over time.
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