Personalizing EFL Learning in Inclusive Classrooms: The Role of Generative AI in Differentiated Instruction - Putri Azzahra Henda Puspitasari; Mia Amaliah; Andang Saehu; Cipto Wardoyo; Dedi Sulaeman (Ma'soem University, 2026)
Generative AI has emerged as a promising tool for addressing learners diversity in English as a Foreign Language (EFL) classrooms. However, previous studies have primarily examined the use of generative AI to support language learning, leaving a limited understanding of how generative AI supports differentiated instruction in inclusive EFL contexts. This study aims to investigate how generative AI supports differentiated instruction in inclusive EFL classrooms by addressing learners' diverse readiness levels, interests, and learning profiles. Employing a qualitative descriptive design, the study involved six EFL teachers from different schools who were purposively selected for their experience implementing generative AI in inclusive EFL classrooms. Data were collected through semi-structured interviews, classroom observations, and document analysis, and were analyzed using thematic analysis. The findings reveal that generative AI assists teachers in adapting instructional content, learning processes, and assessment to accommodate learners' diverse needs. It also enabled teachers to foster student engagement by providing adaptive feedback, personalized learning experiences, and multimodal learning resources. Nevertheless, the implementation of generative AI continues to face three challenges, including limited technological infrastructure, insufficient teacher training, and concerns regarding excessive reliance on AI. These findings contribute to the growing understanding of AI-assisted differentiated instruction and provide practical insights for integrating generative AI into inclusive EFL teaching.