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<p>Gender bias in artificial intelligence (AI) applications has recently gained increasing attention in academia. While some studies have explored gender disparities in specific contexts, there is a lack of comprehensive analysis on how AI-driven gender biases impact female learners across different educational settings worldwide. This systematic review examines research on gender bias in AI applications used in education across various global regions, following PRISMA guidelines. It synthesizes findings from 23 empirical and conceptual papers published between 2019 and 2024—a period marked by the rapid integration of AI technologies in education. The review reveals that research on AI gender bias in educational contexts remains limited, with most studies originating from North America and Western regions. At the same time, South Asia, South America, and Africa receive comparatively little attention. Second, the focus of existing studies is on higher education, neglecting other educational levels. Third, the review found several methodological gaps in the reviewed literature that need improvement. Lastly, the results highlight persistent gender biases in AI tools, particularly in content generation, assessments, and feedback systems. The presence of these biases in the educational context discourages female participation and poses a significant challenge to achieving gender equality in education. The review emphasizes the need for more research in the neglected regions and educational levels to highlight the female issues with AI usage in general. The review also recommends inclusive AI development to foster equitable educational practices and mitigate gender bias in AI-driven learning environments.</p>

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Female gender bias in artificial intelligence applications for education: a systematic review of regional disparities and equity implications

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • Usama Kalim,
  • Asha Kanwar,
  • Lin Xu,
  • Huixin Xu,
  • Xiao Chen,
  • Sadia Irfan,
  • Abida Rasool

摘要

Gender bias in artificial intelligence (AI) applications has recently gained increasing attention in academia. While some studies have explored gender disparities in specific contexts, there is a lack of comprehensive analysis on how AI-driven gender biases impact female learners across different educational settings worldwide. This systematic review examines research on gender bias in AI applications used in education across various global regions, following PRISMA guidelines. It synthesizes findings from 23 empirical and conceptual papers published between 2019 and 2024—a period marked by the rapid integration of AI technologies in education. The review reveals that research on AI gender bias in educational contexts remains limited, with most studies originating from North America and Western regions. At the same time, South Asia, South America, and Africa receive comparatively little attention. Second, the focus of existing studies is on higher education, neglecting other educational levels. Third, the review found several methodological gaps in the reviewed literature that need improvement. Lastly, the results highlight persistent gender biases in AI tools, particularly in content generation, assessments, and feedback systems. The presence of these biases in the educational context discourages female participation and poses a significant challenge to achieving gender equality in education. The review emphasizes the need for more research in the neglected regions and educational levels to highlight the female issues with AI usage in general. The review also recommends inclusive AI development to foster equitable educational practices and mitigate gender bias in AI-driven learning environments.