Language does more than reflect social reality—it actively constructs and perpetuates it. This article examines the intricate ways in which linguistic patterns encode, reinforce, and normalize gender stereotypes, with a focus on the implications for education and the training of artificial intelligence (AI) language models. Drawing on cross-linguistic examples from English, Polish, and Romance languages, the study explores how grammatical gender (Braun, 2001), gendered occupational nouns (Pauwels, 2003), asymmetrical address terms, and stereotypical collocations contribute to the linguistic reproduction of normative gender roles.

Using a mixed-methods approach that integrates critical discourse analysis and corpus linguistics (Baker, 2006; Lazar, 2005), the article demonstrates how such patterns are absorbed and amplified by AI systems trained on large-scale language data. AI language models such as GPT and BERT, though ostensibly neutral, often replicate and even intensify gender bias due to their reliance on corpora steeped in historically gendered narratives (Bender et al., 2021; Zhao et al., 2018). This has tangible consequences for educational content delivery, language learning technologies, and classroom discourse, particularly as AI-generated materials and chatbots are increasingly integrated into pedagogical settings.

The article concludes by advocating for critical linguistic awareness in both language education and AI development. It argues that educators, curriculum designers, and AI developers must collaborate to identify and mitigate gender biases in language input, promote inclusive linguistic practices, and foster critical literacy skills among learners (Eckert & McConnell-Ginet, 2013; Sunderland, 2004). In doing so, we may begin to unbind the words that silently shape—and limit—our collective understanding of gender.

Ackowledgement:
The work is partly based on the research informing the creation of an e-learning course for teachers and parents fostering gender equality in primary and secondary science education: "Digital Bridges over the Gender Gap" cofunded by the EU.

References:
[1] Baker, P. (2006). Using corpora in discourse analysis. Continuum.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 [2] ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
[3] Braun, F. (2001). The communication of gender in the German language: Issues and options. Gender and Language in the Modern Languages Curriculum, 9–24.
[4] Eckert, P., & McConnell-Ginet, S. (2013). Language and gender (2nd ed.). Cambridge University Press.
[5] Lazar, M. M. (Ed.). (2005). Feminist critical discourse analysis: Gender, power and ideology in discourse. Palgrave Macmillan.
[6] Mills, S. (2008). Language and sexism. Cambridge University Press.
[7] Pauwels, A. (2003). Linguistic sexism and feminist linguistic activism. In J. Holmes & M. Meyerhoff (Eds.), The handbook of language and gender (pp. 550–570). Blackwell.
[8] Sunderland, J. (2004). Gendered discourses. Palgrave Macmillan.
[9] Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018). Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 2, pp. 15–20). https://doi.org/10.18653/v1/N18-2003

Keywords: Gender eqality, education, discourse analysis.