Exploring EFL Students’ Experiences with NotebookLM in Professional Cover Letter Writing

Abstract

The integration of artificial intelligence through Google NotebookLM’s Retrieval-Augmented Generation (RAG) architecture offers sophisticated, context-aware guidance for English as a Foreign Language (EFL) learners. Despite burgeoning scholarly interest in generative AI, empirical investigations examining students’ lived experiences with NotebookLM during high-stakes professional writing tasks, such as cover letter composition, remain significantly sparse. Addressing this empirical lacuna, this qualitative descriptive study investigated students’ experiences utilizing NotebookLM for cover letter generation within a vocational curriculum. Employing purposive sampling, eighteen students from two Job Hunting classes were selected, with qualitative data gathered through semi-structured interviews, classroom observations, and textual document analysis. The thematic findings reveal that NotebookLM functions as an effective digital micro-scaffold operating within the students' Zone of Proximal Development (ZPD). Specifically, the platform optimized professional genre awareness regarding cover letter conventions, enriched workplace-specific vocabulary, mitigated writing anxiety, and heightened writing self-efficacy. While source-grounded, document-based interaction was highly evaluated, participants simultaneously underscored the crucial necessity of critical literacy to navigate contextual accuracy. These insights contribute to the expanding discourse surrounding the pedagogical integration of AI-assisted writing tools in vocational EFL education by offering granular empirical evidence on how source-constrained architectures streamline professional genre writing without inducing extraneous cognitive load.