Enhancing EFL students’ speaking skills through AI-integrated shadowing using Loora - Eva Meidi Kulsum; Rina Mutiarawati; Revi Nur Ridwan; Sabila Agnia (Universitas Islam Negeri (UIN) Syekh Wasil, Kediri, Indonesia, 2026)
The growing emphasis on technology-enhanced language learning has highlighted the need to better understand the integration of traditional speaking techniques with artificial intelligence in EFL contexts. This study investigates the implementation of the shadowing technique integrated with AI to enhance students’ speaking skills using a mixed-method approach. Data were collected from 25 pre-service English teachers through speaking tests, Likert-scale questionnaires, and interviews, and analyzed using descriptive statistics, paired-sample t-tests, and thematic analysis. The results reveal that students’ speaking performance significantly improved, as indicated by higher post-test scores and statistically significant differences between pre-test and post-test results. In addition, students demonstrated positive perceptions across all indicators, particularly in speaking skill improvement, highlighting gains in fluency, pronunciation, and natural speaking ability. These findings indicate that the integration of shadowing with Loora provides both effective practice and meaningful learning experiences.