Algorithmic Bias and Educational Justice in the Age of Artificial Intelligence: Social Implications and Policy Solutions in Iran

Document Type : Original Article

Authors

1 Ph.D. Student in Information Technology Engineering, Department of Computer Engineering and Information Technology, Faculty of Engineering, University of Qom, Qom, Iran

2 . Assistant Professor, Department of Computer Engineering and Information Technology, Faculty of Engineering, University of Qom, Qom, Iran

10.22091/jrim.2026.14502.1436

Abstract

With the expanding use of artificial intelligence—especially large language models (LLMs)—in education, a key question arises: how can these technologies strengthen or undermine educational equity? This study aims to elucidate the dimensions of algorithmic bias within educational systems and to derive social implications and policy responses appropriate to the Iranian context. The present research is qualitative, adopting a documentary–analytical approach. It employs a systematic content analysis of official reports, scholarly articles, and international case studies, guided by a conceptual checklist of educational equity. The study’s theoretical framework draws on John Rawls’s theory of justice as fairness and Amartya Sen’s capability approach. The findings indicate that bias operating at four levels—problem formulation, data, modelling, and interpretation/implementation—can reproduce educational inequalities and, in Iran’s diverse context marked by a digital divide, further intensify them. Accordingly, six policy directions are proposed: equity-oriented algorithm design; monitoring and ensuring data diversity; providing algorithmic ethics education for stakeholders; strengthening transparency and accountability; developing indigenous models; and reducing the digital divide. The article’s contribution lies in linking theories of justice with the literature on algorithmic bias and in advancing a locally grounded framework for educational equity policymaking in Iran in the age of artificial intelligence.

Keywords

References

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History

  • Receive Date: 13 November 2025
  • Revise Date: 26 December 2025
  • Accept Date: 10 January 2026

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