THE ROLE OF AI PRACTICE IN ENHANCING VOCATIONAL HIGH SCHOOL EFL LEARNERS' SPEAKING SELF-EFFICACY IN YOGYAKARTA
DOI:
https://doi.org/10.24127/pj.v15i2.15868Abstract
Research on the use of artificial intelligence (AI) in language learning has grown rapidly in recent years. Most existing studies focus on higher education and involve multiple intervention sessions. However, empirical evidence regarding the impact of a single-session AI-based intervention on students’ speaking self-efficacy remains very limited. This study examines how AI-assisted speaking practice affects the speaking self-efficacy of EFL students at a vocational high school in Yogyakarta. Using a one-group pretest-posttest pre-experimental design, the study involved 63 students selected through convenience sampling. Data were collected via a speaking self-efficacy questionnaire administered before and after structured speaking practice sessions using ChatGPT, then analyzed using a paired-sample t-test. The results indicate that students demonstrated high levels of speaking self-efficacy across all four dimensions: linguistic self-efficacy, self-regulatory, delivery, and performance. The most significant improvement occurred in the performance self-efficacy dimension, while the smallest improvement was found in the linguistic dimension. These findings indicate that AI has the potential to boost students’ self-efficacy in speaking English and can serve as a valuable tool to support the psychological well-being of EFL learners.
References
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Company.
Celik, O., Yildiz, Y., & Kara, S. (2025). Artificial intelligence in foreign language education: Pedagogical implications and future directions. ELT Research Journal, 14(1), 61–77. doi:10.71362/eltrj.1703861
Efendi, A., & Kristanto, A. (2024). The integration of artificial intelligence in EFL classrooms: Impacts on learner engagement and performance. Journal of English Language Teaching and Linguistics, 9(2), 145–160.
Kusumarasdyati, L. (2020). The use of technology-enhanced learning in Indonesian EFL classrooms. TEFLIN Journal, 31(2), 245–262. doi:10.15639/teflinjournal.v31i2.245-262
Leeming, P., Vitta, J. P., Hiver, P., Hicks, D., McLean, S., & Nicklin, C. (2024). Willingness to communicate, speaking self-efficacy, and perceived communicative competence as predictors of second language spoken task production. Language Learning, 74(4), 917–949. doi:10.1111/lang.12640
Li, X., & Zhang, Y. (2023). Artificial intelligence-supported language learning: Effects on EFL learners’ motivation and self-efficacy. Computer Assisted Language Learning, 36(5–6), 789–805.
Shadiev, R., Yang, M., & Huang, Y. M. (2024). A review of artificial intelligence applications in language education. Smart Learning Environments, 11(1), 1–20. doi:10.1186/s40561-024-00360-3
Yildiz, Y. (2024). Exploring the role of AI-based tools in enhancing EFL learners’ speaking performance. Education and Information Technologies, 29(3), 3125–3142.
Zhang, H. (2022). The relationship between speaking self-efficacy and willingness to communicate in EFL contexts. System, 105, 102735. doi:10.1016/j.system.2022.102735
Zhang, H., Li, J., & Wang, Y. (2025). Artificial intelligence-driven feedback and its impact on L2 speaking development. Computer Assisted Language Learning, 38(1), 55–74.
Zou, D., Luo, S., Xie, H., & Hwang, G. J. (2024). A systematic review of artificial intelligence applications in language education. Educational Technology & Society, 27(1), 1–17.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage.
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505
Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125–132. https://doi.org/10.2307/327317
Huang, L., & Yang, Y. (2023). Artificial intelligence–mediated feedback and its effects on EFL learners’ speaking anxiety and self-efficacy. Computer Assisted Language Learning, 36(8), 1123–1145.
Lamb, M. (2012). A self-system perspective on young adolescents’ motivation to learn English in urban and rural settings in Indonesia. System, 40(4), 481–493. https://doi.org/10.1016/j.system.2012.10.009
Marwan, A. (2007). Investigating students’ foreign language anxiety. Malaysian Journal of ELT Research, 3, 37–55.
Mills, N., Pajares, F., & Herron, C. (2007). Self-efficacy of college intermediate French students: Relation to achievement and motivation. Language Learning, 57(3), 417–442.
Shao, Y. (2025). AI-supported speaking practice, anxiety reduction, and self-efficacy development in EFL learners. System, 118, 102950.
Zhang, X., Li, Q., & Chen, Y. (2025). Psychological dimensions of AI-assisted language learning: A systematic review. Educational Technology Research and Development, 73(1), 233–256.
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