Managing Machine Learning Integration in English Language Curriculum: Challenges and Innovations in Teacher Training

Abstract

The integration of machine learning (ML) into English language teaching is gaining traction due to its potential to support personalized learning and increase student engagement. However, its implementation presents critical challenges. Many educators lack the pedagogical training needed to use ML tools effectively, such as adaptive learning platforms and automated assessment systems. Institutional approaches often remain fragmented and reactive, offering limited support, vague curricular links, and few opportunities for collaboration. Ethical concerns—such as data privacy, algorithmic bias, and diminished teacher autonomy—further complicate the adoption process. This study employs a qualitative library research method to examine recent literature on ML in English education and teacher preparation. Results emphasize the need for inclusive training strategies, including blended learning, mentorship, and AI-focused modules, that enable teachers to use ML tools thoughtfully and ethically. Yet these efforts are still limited in reach, highlighting the importance of systemic reforms that ensure ethical and equitable ML integration across diverse educational contexts.