| Περίληψη: |
This article examines how the rapid rise of generative AI (GenAI) is impacting language teacher education, emphasising the importance of ethical, fair, and unbiased GenAI use. Teachers are tasked with ensuring GenAI-generated content is truthful and accurate, particularly in visual and textual materials. However, existing societal biases, prejudices, and stereotypes can be reproduced by GenAI, perpetuating marginalisation of certain groups. While current research accentuates skills and competency development, how GenAI can reinforce stereotypes related to language, race, and gender, especially in second language (L2) teaching, receives scant attention. To address this, the multiple-case study deploys notions of identity and intersectionality to focus on four Hong Kong L2 teacher candidates (TCs) in two courses with social equity and critical GenAI literacies as the pedagogical goal. Through multimodal projects and critical analysis of GenAI outputs, TCs examined how biases manifest in multimodal outputs to be used as teaching materials. Findings show how TCs can identify and counteract racialised, gendered, cultural, and professional biases by engaging in iterative and counter-discursive prompting and critical reflections. The study underscores the importance of and demonstrates how intersectionality is central to projects and tasks developed for knowledge resistant and creation and thus for epistemic and social equity. [ABSTRACT FROM AUTHOR] |