Speaker
Description
This action research study investigates the transformative integration of generative AI tools and intelligent note-taking platforms to foster learner agency and elevate assessment outcomes in a rural Vietnamese primary school context. The primary purpose of this investigation is to address the critical gap in empirical evidence regarding AI-driven pedagogy, digital literacy, and classroom assessment for young learners in resource-constrained environments. Utilizing an action research methodology consisting of four cyclical phases—planning, acting, observing, and reflecting—the intervention was conducted over one academic semester across six Grade 3 classes. Data were systematically gathered through structured classroom observation checklists, student engagement rating scales, and pre- and post-intervention vocabulary assessments to ensure the validity, fairness, and integrity of the AI-supported learning process. The findings reveal that this AI-enhanced approach not only significantly improved vocabulary retention and oral proficiency but also sparked an unprecedented learning movement within the local community. Most notably, this pedagogical shift culminated in historic competitive success: 14 students qualified for the provincial-level English competition, and three achieved national prizes in the Internet Olympiads of English, marking the first national-level academic accolades in the school's history. The implications of these results suggest that when aligned with robust evaluation frameworks and student autonomy, generative AI tools can effectively bridge the digital divide. This study offers a scalable blueprint and critical classroom-level evidence for language educators aiming to implement sustainable, equitable, and ethically sound Computer-Assisted Language Learning (CALL) practices in underdeveloped regions.
Keywords: learner agency, primary ELT, AI-driven assessment, academic achievement, CALL
