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Unpacking Cultural Bias in AI Language Learning Tools: An Analysis of Impacts and Strategies for Inclusion in Diverse Educational Settings

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  • André Alexus Lewis

    (Department of Language and Culture Studies Faculty of Education and Humanities University of Guyana)

Abstract

AI programs that help us learn languages are commonly used in various classrooms; however, they often include some forms of prejudice preventing their productivity. This research scrutinizes them meticulously by following up on the results obtained from questionnaires, interviews, and the analysis of data derived from four artificial intelligence platforms. It was found that there is a reduction of more than 30% in the participation of minority students when such biases are clear, signaling alarms for extra comprehensive datasets on which to base further research in the field of AI. It suggests a proposed framework called ‘METAL’ (Multicultural Education through Technology Assisted Learning) aimed at promoting multiculturalism within these apps amongst others. The suggested ways forward comprise integrating multi-cultural content as well as adopting adaptive algorithms that respect specific customs situated in various societies worldwide. Therefore, there is a need for AI tools redesigned to cater to all learners’ needs, thus enhancing educational development as a whole.

Suggested Citation

  • André Alexus Lewis, 2025. "Unpacking Cultural Bias in AI Language Learning Tools: An Analysis of Impacts and Strategies for Inclusion in Diverse Educational Settings," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 1878-1892, January.
    • Handle: RePEc:bcp:journl:v:9:y:2025:i:1:p:1878-1892
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    References listed on IDEAS

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    1. Sdenka Zobeida Salas-Pilco & Kejiang Xiao & Jun Oshima, 2022. "Artificial Intelligence and New Technologies in Inclusive Education for Minority Students: A Systematic Review," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
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