AI-DRIVEN PERSONALIZED LEARNING IN THE EFL CONTEXT: EXPLORING STUDENTS’ ENGAGEMENT AND MOTIVATION AT TERTIARY LEVEL EDUCATION

In an era where Artificial Intelligence (AI) is reshaping educational landscapes, this study investigates the impact of AI-driven personalized learning on students‘ engagement and motivation in English as a Foreign Language (EFL) classroom at the tertiary education in Khulna, Bangladesh. A mixed-method design was used to collect the primary data, comprising both survey and semi structured interviews, from undergraduate EFL learners enrolled in three different universities. The findings reveal positive associations between AImediated personalization and critical dimensions of learning, namely motivation, engagement, and self-regulation. This empirical evidence substantiates that personalized AI technologies markedly foster learners‘ emotional investment, sustained effort, and autonomous strategic regulation within the context of language acquisition. However, the qualitative data outlined an important paradox: while students opposed exploitation and risk in AI, they stressed its inability to supply emotional support as well as cultural clues and context awareness so that under such considerations there was no substitute for a teacher. Theoretically, this study unifies ZPD, Mastery Learning, and the L2 Motivational Self System while introducing new concepts that clarify both the potential and limitations of AIassisted education. Nevertheless, to address the limitation of this study, future studies can undertake longitudinal designs in various settings and disciplines while exploring potential evolving nature of human-AI pedagogical partnerships to confirm the long-lasting effects of these motivational and engagement outcomes. Emphatically, teachers may assign AIsupported homework to build study habits, then use class time for interactive, collaborative tasks that deepen engagement. 

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