Predictive Power of AI-Generated Corrective Feedback on EFL Learners’ Self-Regulation and Language Proficiency: Empirical Evidence from Saudi Arabian Context
Year: 2025
Type: article
Study design: Observational Study
Abstract: Students learning English as a foreign language (EFL) often face challenges in achieving both linguistic accuracy and fluency, primarily due to the absence of prompt and personalized feedback. AI-generated corrective feedback (AIGCF) offers a promising opportunity by providing timely, tailored support designed to enhance learner autonomy and language proficiency. This study examined the predictive relationships between AIGCF, self-regulation, and language proficiency within a sample of 125 undergraduate students majoring in English Language and Literature at universities in Saudi Arabia, utilizing a non-interventional correlational design and multiple regression analysis. Data were collected utilizing dependable instruments, including a self-regulation questionnaire, a language proficiency assessment, and a perception scale for AIGCF. The Pearson correlation analyses revealed significant positive relationships between AIGCF and self-regulation, AIGCF and language proficiency, and between self-regulation and language proficiency. The findings from the multiple regression analysis indicated that AIGCF and self-regulation together accounted for 36% of the variance in language proficiency. Self-regulation was identified as the more substantial predictor when compared to AIGCF. The findings suggest that students who engage positively with AIGCF are likely to exhibit enhanced self-regulatory behaviors, which in turn contribute to higher proficiency levels. This study, situated within the evolving educational landscape of Saudi Arabia and aligned with the digital goals of Vision 2030, provides empirical evidence highlighting the significance of integrating AIGCF and self-regulation training into EFL programs.
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7216779481