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Effects of a Culturally Embedded AI-Powered Speaking Application on EFL Proficiency and Retention Among Indonesian University Students | ||
| Applied Research on English Language | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 18 مهر 1405 | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22108/are.2026.149268.2787 | ||
| نویسندگان | ||
| Adam Adam* ؛ Sri Sugiharti؛ Annisa Putri Melchan | ||
| English Education Study Program, Universitas Riau Kepulauan, Batam, Indonesia | ||
| چکیده | ||
| Abstract: Many Indonesian university students find speaking English difficult because of anxiety and few opportunities to practice. This study evaluated VozVibe, an Android application that gives automated feedback on learners' speech using automatic speech recognition (ASR) and a large language model (LLM), sets speaking tasks in familiar Indonesian contexts, and uses group-based gamification framed around the Indonesian principle of Gotong Royong (mutual assistance). Its design drew on the Affective Filter Hypothesis, Self-Determination Theory, and sociocultural theory. In a randomized controlled trial, all 72 third-semester English Department students taking a speaking course at an Indonesian university in 2025/2026 were randomly assigned to VozVibe (n = 36) or conventional instruction (n = 36) for 16 weeks. Speaking was tested before, immediately after, and about three months after the intervention. A linear mixed model showed larger improvement in the VozVibe group at the immediate (d = 0.60) and delayed (d = 0.96) post-tests; the later advantage came mainly from the larger gain during the intervention rather than from less forgetting. Questionnaires and interviews suggested that students found the familiar tasks less stressful and valued the group features and self-paced progression. Because the application was compared as a whole with regular classes, the effect of AI feedback cannot be separated from that of additional practice. Culturally embedded AI applications can give students more speaking practice and feedback than a conventional class, and delayed post-tests are needed to check whether such gains last. | ||
| کلیدواژه‌ها | ||
| adaptive gamification؛ automatic speech recognition؛ large language models؛ mobile-assisted language learning؛ skill attrition | ||
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