Review Article
Ziying Peng, Arumugam Raman
CONT ED TECHNOLOGY, Volume 18, Issue 1, Article No: ep631
ABSTRACT
Aim: This systematic literature review (SLR) critically examines the impact of blended learning (BL) in English as a foreign language (EFL) education, with a focus on methodological rigor and research gaps.
Background: Although previous reviews have underscored the advantages of BL for EFL learners, many have been limited in scope, focused on narrow outcome measures, or insufficient methodological clarity. This review updates and extends earlier work by integrating studies published from 2020-2025, while assessing the methodological robustness of included studies.
Design: SLR following preferred reporting items for systematic reviews and meta-analyses guidelines.
Methods: Peer-reviewed articles published from January 2020 to April 2025 were identified through Scopus, Web of Science, and China national knowledge infrastructure. Inclusion criteria required interventions involving BL with EFL students, comparison groups, and reported learning outcomes. Methodological quality was evaluated using the mixed methods appraisal tool.
Results: Thirty studies met the inclusion criteria. Findings suggest that BL exerts beneficial effects across five key areas: academic performance, learning engagement and motivation, learner autonomy, psychological well-being, and learning satisfaction. However, overreliance on quasi-experimental designs, convenience sampling, and short intervention durations undermines generalizability. Few studies explored mental health and critical thinking outcomes.
Conclusions: BL has shown promising results in EFL contexts, but stronger empirical designs are needed. Future research should focus on randomized controlled trials, cross-regional studies, and theoretical grounding to ensure a robust evidence base. Educators are encouraged to incorporate BL strategically to foster improvements in writing skills and critical thinking.
Keywords: blended learning, EFL education, EFL students, systematic literature review
Research Article
Liwei Hsu
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep619
ABSTRACT
This study examines the use of generative artificial intelligence, i.e., ChatGPT, in English as a foreign language (EFL) learning, emphasizing the mediating role of entangled cognition and the effects of the learning outcomes of the tourism students. The research was designed to a quasi-experiment which included 96 participants (48 in an experimental group and 48 in a control group) who were sampled based on convenience to the Spring 2024 semester in one university in southern Taiwan. The “custom virtual language course” experimental group used ChatGPT for personalized language practice and culture learning, control group received traditional learning. A questionnaire package, including the cognitive technology use questionnaire (CTUQ), extended mind scale (EMS), distributed cognition questionnaire (DCQ), metacognitive awareness inventory (MAI), and TOEIC pre- and post-tests was administered to collect the data. The difference-in-differences design was adopted and observed a significant treatment effect such that the treatment group had an average increase in mean scores of 37.98 (standard deviation [SD] = 7.80) compared to 19.62 (SD = 7.80) for the control group and, therefore, an average treatment effect of 21.38 (95% confidence interval [18.74, 24. 01]). Findings suggest that ChatGPT promotes cognitive offloading, distributed cognition, and metacognitive awareness (CTUQ mean [M] = 3.701, EMS M = 3.421, DCQ M = 3.721, MAI M = 3.551), and the development of collaborative learning and cultural competence. These results reveal ChatGPT’s potential to reform EFL education, but they also indicate the necessity to mitigate the risks associated with ethical quandaries and over-dependence. Future studies need to create specific scales that can be used for entangled cognition and examine the long-term effects on cognition.
Keywords: ChatGPT, difference-in-differences approach, EFL education, entangled cognition, generative artificial intelligence