Bias in AI-based L2 learning tools
Turdalieva Feruza Rustamjon Qizi
Tashkent Financial Institute
Abstract
AI-based L2 learning tools, while promising personalized and engaging language learning, harbor potential for bias. This abstract explores the types of bias (data, algorithmic, feedback), their consequences (unequitable learning, reinforced stereotypes, ethical concerns), and mitigation strategies (data diversification, algorithmic auditing, inclusive feedback, user education). Contextual awareness and human oversight are crucial for ethical and equitable use. Addressing bias is an ongoing challenge in AI-based L2 learning, but by being aware and proactive, we can ensure these tools contribute to a more inclusive and equitable language learning experience for all.
