AI-enhanced professional learning communities: a new era of personalized teacher education

Language teacher education programs can become more reflective, inclusive, collaborative, situated, and inquiry-based. One such professional approach to incorporate these characters can be through personalized language teacher education (PLTE). Due to the importance of using AI and professional learning communities (PLCs) for developing a personalized teacher education, this study explored how AI-enhanced PLCs could be leveraged to create a more responsive, inclusive, and personalized teacher education. Still, a significant gap exists in understanding how AI can be specially integrated into PLCs to create personalized pathways for ELT preservice teachers, mainly in under-resourced contexts. To conduct this exploratory case study, 8 Iranian English language teaching (ELT) pre-service teachers were purposively selected from a teacher education university. Data was collected from group discussion, artifacts, and interviews, and the result of the thematic analysis revealed that AI-enhanced PLCs fostered personalized, reflective, and collaborative development by addressing individual teaching needs and providing innovative strategies. By addressing individual teaching needs and providing innovative instructional strategies, AI facilitated a dynamic learning environment. However, effective integration required overcoming challenges like limited AI literacy and contextual mismatches, highlighting the potential for tailored, impactful education. This study can inform teacher educators, policymakers, administrators, and teachers to integrate AI into their PLCs to develop a PLTE.