Exploring the Influence of Generative AI on Self-Regulated Learning: A Mixed-Methods Study in the EFL Context
DOI:
https://doi.org/10.25217/ji.v10i2.6389Keywords:
Adaptive Strategies, EFL Learning, Generative AI, Self-Regulated LearningAbstract
Given the transformative impact of Generative AI (GenAI) on education, this study investigates its specific influence on the distinct phases of students' Self-Regulated Learning (SRL) within an English as a Foreign Language (EFL) context. Despite its ubiquity, a gap exists in understanding how students practically use GenAI to self-regulate their learning and what adaptive strategies they employ. This study utilized a mixed-methods approach to explore this phenomenon. Participants were 100 undergraduate EFL students in an Indonesian university with at least three months of experience using ChatGPT. Data were collected through an adapted SRL questionnaire, semi-structured interviews, and Focus Group Discussions (FGDs). Quantitative data were analyzed using Pearson correlation and ANOVA, while qualitative data underwent thematic analysis. The findings revealed a significant positive correlation between GenAI use and overall SRL (r = .55), although its influence was strongest on the forethought (planning) phase and markedly weaker on the self-reflection phase. Qualitatively, students devised adaptive strategies such as dynamic scaffolding and learner-driven fading to foster independence. However, these were often counteracted by hindering factors, primarily cognitive offloading, the illusion of competence, and a widespread deficit in critical digital literacy. Theoretically, this study contributes by articulating how GenAI reshapes core learning processes, proposing necessary extensions to established frameworks of self-regulation and sociocultural learning . The pedagogical implications are profound, demanding a curricular shift towards foundational critical digital literacy and a fundamental redesign of assessment to prioritize process over product.
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