The Impact Mechanism of Algorithmic Bias in Educational Recommendation Systems on Educational Process Fairness from an Algorithm Governance Perspective and the Construction of a Four-Dimensional Audit Framework

Xueqi Li

doi:10.70088/dvsfqb55

Authors

Xueqi Li School of Education, Xizang University, Lhasa, China Author

DOI:

https://doi.org/10.70088/dvsfqb55

Keywords:

algorithm bias, fairness in educational process, educational recommendation system, algorithm governance

Abstract

From an algorithm governance perspective, this study integrates the sociology of technology and educational equity theory. Utilizing literature review, logical deduction, and framework construction, it analyzes the generative logic and manifestations of algorithmic bias in educational recommendation systems, and reveals how such bias affects educational opportunities. The findings indicate that bias originates from skewed data collection, flawed model design, reinforcing interaction feedback, and weak institutional constraints. Through a triple mechanism of "copying, reinforcing, and solidifying", this bias creates "digital red line" barriers in resource accessibility, learning path planning, and developmental opportunity acquisition, thereby exacerbating intergroup educational inequality. Accordingly, a four-dimensional algorithm auditing framework is constructed, covering technology, process, outcome, and guarantee, with four specific auditing modules: data quality, model fairness, educational impact, and governance mechanisms. A multi-party collaborative governance pathway is also proposed. This research provides theoretical support and practical tools for mitigating risks to educational process fairness in the intelligent era, and promotes the transformation of educational recommendation systems from instruments of "personalized empowerment" to enablers of "fairness empowerment".

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