Educational AI and the Politics of Fairness: Structural Bias, Governance, and Student Empowerment

Zhengrui Cui

doi:10.54097/6khfyx66

Authors

Zhengrui Cui

DOI:

https://doi.org/10.54097/6khfyx66

Keywords:

Educational artificial intelligence; algorithmic recommendation; student modelling; cognitive training; educational equity.

Abstract

With the widespread application of artificial intelligence systems in the field of education, algorithmic recommendations and student modelling are gradually replacing teachers and course designers in traditional teaching, becoming the core tools for the allocation of educational resources. However, the recommendation systems of existing educational platforms, while pursuing personalisation and improving efficiency through technological means, may exacerbate educational inequality. This paper analyses how educational platforms allocate resources through algorithms and examines recommendation systems to reveal the current state of student modelling and predictive pathways, particularly how algorithms use historical data and label features to shape students' learning trajectories. Subsequently, this paper explores the cognitive power structures underlying these predictive mechanisms and how algorithms profoundly influence students' self-perception and learning choices through data classification, ranking, and comparison. In summary, based on the above analysis, this paper proposes improvement suggestions from three aspects: policy, platform, and education, to enhance algorithm transparency and interpretability, design more inclusive learning paths, ensure educational equity, and protect students' cognitive autonomy.

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