Language education amid digital disruption: in-service and pre-service teacher perspectives on AI language bias and curriculum integration

Abstract

This study examines the perceptions of pre-service teachers (PSTs) and in-service teachers (ISTs) towards artificial intelligence (AI) in foreign language education, focusing on its inherent linguistic biases and its integration into teaching practices. A mixed-methods approach was employed, combining survey data from 271 participants at the primary and secondary school levels across Germany, Spain, and T & uuml;rkiye with qualitative thematic analysis. t-Tests, U-tests, Shapiro-Wilk tests, and Levene tests were employed in the quantitative analysis. The findings suggest that both PSTs and ISTs demonstrate moderate awareness of the potential of AI to perpetuate standard language ideologies, with ISTs exhibiting greater sensitivity to the limitations of strict language norms. Low familiarity with AI in both groups underscores the need for targeted training programmes and continuous professional development. While both groups expressed positive attitudes towards AI integration, differing pedagogical priorities emerged: PSTs focused on the use of AI for resource development, whereas ISTs emphasised enhanced student engagement. This study strongly advocates for AI literacy training tailored to language educators, promoting a plurilithic approach to AI design and implementation. It also calls for teacher education programmes to incorporate discussions on language ideologies within AI integration to foster inclusive and equitable teaching practices.