Learner Agency in AI-Supported EFL Speaking The Interplay of Local Identity, Self-Regulation, and Oral Communicative Competence among Sasak Learners

Hasbullah (Qamarul Huda Badaruddin University)
Mawardi (Nahdawatul Wathan Mataram University)
Nurlensi (Institut Agama Hindu Negeri Tampung Penyang Palangkaraya)

Journal of Educational Studies
Vol. 3 No. 3 (2025): Desember

Artificial intelligence (AI) has created new opportunities for English as a foreign language (EFL) speaking practice, yet the effectiveness of AI-supported learning may depend on learners’ agency, self-regulation, and sociocultural identity. This study investigates learner agency in AI-supported EFL speaking by examining the interplay among learner agency, self-regulation, local identity, and oral communicative competence among Sasak learners in Indonesia. An explanatory sequential mixed-methods design was employed with 120 EFL learners participating in an eight-week AI-supported speaking program. Quantitative data were collected through measures of learner agency, self-regulation, local identity, and oral communicative competence, while qualitative data were obtained through learner interviews and reflection logs. The quantitative findings showed positive relationships among all four variables, with self-regulation demonstrating the strongest association with oral communicative competence. Regression analysis further indicated that learner agency, self-regulation, and local identity each contributed significantly to oral communicative competence, with self-regulation emerging as the strongest predictor. Learners also demonstrated improvement in oral communicative competence following the instructional period. Qualitative findings explained these patterns by showing that learners used AI to exercise greater control over speaking practice, monitor their performance, revise responses, and engage in repeated practice. Local identity provided culturally familiar content that facilitated idea generation and meaningful participation in English. Overall, the findings suggest that AI-supported EFL speaking is most meaningfully understood as an interaction among technological affordances, learner agency, self-regulation, and sociocultural resources. The study highlights the importance of designing culturally responsive AI-supported speaking activities that promote learner choice, self-regulated learning, and meaningful communication.