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Quasi-experimental design improved semantic learning outcomes in EFL students, suggesting chatbot support boosts engagement.
This study explores the impact of an innovative approach that combines artificial intelligence (AI) chatbot support with collaborative note-taking (CNT) in the comprehension of semantic terms among English as a Foreign Language (EFL) learners. Given the significance of semantics in English language learning, traditional didactic methods often present challenges for EFL learners. The proposed AI chatbot-supported approach aims to foster learner interaction, while the CNT strategy focuses on enhancing knowledge retention and engagement with learning materials. Conducted as a quasi-experimental pre-test-post-test design, the study involved 60 English Language and Literature majors from a non-English-speaking area enrolled at a private university. Participants were divided into the AI chatbot-supported and CNT (AI-CNT) group and the conventional CNT (cCNT) group. Results indicated that the AI-CNT group outperformed the cCNT group across various dimensions of semantic learning outcomes, including performance, achievement, self-efficacy, metacognition, and anxiety reduction. This study highlights the potential of integrating AI chatbot support and the CNT strategy to significantly enhance the EFL semantic learning experience. The personalized and interaction-based linguistic practices, enriched with feedback and emotional support, offer a promising avenue for advancing language learning outcomes in the digital age.
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Mei‐Rong Alice Chen (2024) studied this question.
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