Artificial Intelligence as a Pedagogical Bridge in Education Reform: Localizing EFL Listening Materials through Generative AI in Resource-Constrained Contexts

Abstract

Listening instruction in many English as a Foreign Language (EFL) contexts continues to rely on globally produced audio materials that are linguistically accurate but culturally distant from learners’ lived experiences. Contextualized pedagogy is widely advocated, yet teachers often lack the resources to produce high-quality localized audio. This study investigates how generative artificial intelligence (AI) supports the development of contextualized listening materials and influences students’ listening experiences. A qualitative multi-site case study was conducted in eight secondary schools across geographically diverse areas of West Kalimantan, Indonesia. English teachers and 32 students participated in the design and classroom implementation of AI-supported listening materials. Data were collected through pre- and post-development teacher interviews, classroom reflections, and post-implementation student interviews. The data were analyzed thematically and compared across cases. The findings show that generative AI functioned not merely as a productivity tool but as a form of pedagogical infrastructure. Through iterative prompt refinement and contextual calibration, teachers transformed local knowledge into pedagogically appropriate listening materials. Students reported improved comprehension, reduced cognitive load, greater engagement, and stronger connections with materials reflecting familiar environments. The study contributes to AI-in-education research by demonstrating how generative AI can strengthen teacher agency and support contextualized listening pedagogy in resource-constrained settings. It highlights the potential of AI to bridge technological feasibility and locally grounded language instruction, contributing to more relevant, inclusive, and responsive EFL education. The study recommends that teacher professional development and reform-oriented artificial-intelligence adoption prioritize prompt-mediated pedagogical reasoning and locally grounded material design.