Integration of Adaptive AI Conversational Models in Bipa Learning: Design, Implementation, and Effectiveness Evaluation
Abstract
Objective: This study aims to evaluate the efficacy of integrating adaptive conversational AI models in enhancing language proficiency—specifically speaking and listening skills and learning motivation among BIPA (Indonesian for Foreign Speakers) learners. Methods: A quasi-experimental design was employed involving 30 BIPA learners at ESGC São João de Brito, Liquica, Timor-Leste. Participants were divided into an experimental group utilizing an adaptive conversational AI model and a control group following conventional teaching methods. Data collection encompassed language proficiency tests (speaking and listening), questionnaires to gauge learning motivation, and direct classroom observation. Results: The findings revealed significant improvements in the experimental group's speaking and listening abilities compared to the control group. Furthermore, learners using AI exhibited higher motivation levels, engaged in more active classroom interactions, and held positive perceptions of the AI-based approach, whereas the control group did not demonstrate comparable gains. Novelty: This research underscores the distinct potential of adaptive conversational AI not only for improving oral communication but also for fostering intercultural competence among BIPA learners. It offers valuable insights for designing innovative, responsive, and learner-centered teaching strategies within technology-integrated BIPA curricula.
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