AI-Assisted Error Analysis for Improving EFL Writing Accuracy: A Corpus-Based Quasi-Experimental Study
Keywords:
AI-assisted feedback, error analysis, corpus linguistics, EFL writing, learner autonomyAbstract
This study investigates the effectiveness of AI-assisted error analysis in improving English as a Foreign Language (EFL) learners’ writing accuracy in a Libyan university context. Adopting a quasi-experimental, corpus-based design, the study involved two intact groups of undergraduate EFL students (n = 60). The experimental group received AI-supported corrective feedback using tools such as Grammarly and ChatGPT, while the control group was taught through traditional teacher-centered feedback.
A learner corpus consisting of pre-test and post-test essays was compiled to examine patterns of linguistic errors over time. Quantitative data were analyzed using paired and independent samples t-tests, while corpus-based error analysis was employed to identify and categorize recurring errors.
The results revealed that the experimental group significantly outperformed the control group in post-test writing performance (p < 0.001), with a large effect size indicating a strong instructional impact. Corpus findings further demonstrated a substantial reduction in grammatical errors, particularly intense usage, prepositions, and sentence structure. However, improvements in lexical accuracy were comparatively moderate.
The findings suggest that AI-assisted feedback is particularly effective in enhancing rule-based aspects of writing by increasing learners’ noticing of linguistic errors and facilitating iterative revision. Nevertheless, the results also indicate that some learners relied on AI feedback without sufficient critical engagement, highlighting the continued importance of teacher mediation in AI-supported writing instruction.
Overall, the study contributes to the growing field of AI-enhanced language learning by integrating corpus linguistics, error analysis, and experimental methods, offering empirical evidence from an under-researched EFL context.
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