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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 26, No. 0, pp.1164-1181
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 30 Sep 2026
Received 09 Jul 2026 Revised 29 Jul 2026 Accepted 30 Jul 2026
DOI: https://doi.org/10.15738/kjell.26..202609.1164

Narrow Reading with AI-Generated Texts for L2 Vocabulary Learning: Exploring the Roles of Vocabulary Size and Working Memory

Eun Young Kang
Professor, Division of Liberal Arts Kongju National University 1223-24, Cheonan-daero, Seobuk-gu, Cheonan-si, Chungcheongnam-do, Korea, Tel: +82-41-850-8660 ekang@kongju.ac.kr


© 2026 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study examined theme-based narrow reading (NR) with AI-generated texts for L2 vocabulary learning and whether vocabulary size and working memory (WM) predicted gains. In a quasi-experimental pretest-posttest design, 96 Korean EFL university students were assigned to a narrow reading or comparison group. Over six sessions, the NR group read AI-generated texts on one topic (measles outbreak), while the comparison group read texts on unrelated topics. Knowledge of eight target words was assessed through meaning recall and form recognition tests at pretest, immediate posttest, and delayed posttest. A repeated measures ANCOVA revealed a significant main effect of group, with the NR group outperforming the comparison group at both time points after controlling for pretest scores. The NR group’s advantage was maintained over a two-week delay. Regression analyses within the NR group showed that both vocabulary size and WM predicted immediate gains, but only WM predicted delayed gains, suggesting a more enduring role in retention. These findings demonstrate that AI-generated texts offer a practical, pedagogically principled alternative to authentic NR materials, enabling precise control over vocabulary frequency and thematic coherence. The results also underscore the importance of WM—as a driver of learners’ attention to target lexis—in vocabulary learning.

Keywords:

narrow reading, AI-generated texts, L2 vocabulary learning, EFL, individual differences

Acknowledgments

This work was supported by the research grant of Kongju National University in 2025.

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