AI-Powered EFL Speaking Education: Integrating Metacognitive Scaffolding Strategies
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This study explores the impact of AI-powered language learning on learners’ speaking performance, employing Vygotsky’s constructivist approach by integrating scaffolding into metacognitive strategies. A mixed-method research design was implemented to gain an in-depth understanding of this relationship. Data were collected from 103 students and 10 instructors using open-ended questionnaires and the Metacognitive Awareness Speaking Questionnaire (MASQ), and were then analyzed through content analysis and one-way ANOVA. The results indicate that students generally hold a positive view of AI’s ability to provide prompt feedback and boost confidence in English conversations. However, both students and instructors expressed concerns about an over-reliance on AI, fearing that its excessive convenience could hinder meaningful language internalization. Furthermore, a few instructors suggested that AI might inhibit learners' cognitive development in language learning. Regarding metacognitive scaffolding strategies, findings from MASQ show that learners primarily use AI for ‘awareness’ rather than ‘monitoring,’ highlighting the need for more structured support through diverse scaffolding techniques. Based on these findings, the pedagogical implications of using AI as a metacognitive scaffolding tool in language learning emphasize the need for educators to carefully integrate AI tools into the learning process. This integration should ensure that AI not only promote learners’ awareness of their learning but also encourage active monitoring and regulation of their learning strategies.
Abstract
This study explores the impact of AI-powered language learning on learners’ speaking performance, employing Vygotsky’s constructivist approach by integrating scaffolding into metacognitive strategies. A mixed-method research design was implemented to gain an in-depth understanding of this relationship. Data were collected from 103 students and 10 instructors using open-ended questionnaires and the Metacognitive Awareness Speaking Questionnaire (MASQ), and were then analyzed through content analysis and one-way ANOVA. The results indicate that students generally hold a positive view of AI’s ability to provide prompt feedback and boost confidence in English conversations. However, both students and instructors expressed concerns about an over-reliance on AI, fearing that its excessive convenience could hinder meaningful language internalization. Furthermore, a few instructors suggested that AI might inhibit learners' cognitive development in language learning. Regarding metacognitive scaffolding strategies, findings from MASQ show that learners primarily use AI for ‘awareness’ rather than ‘monitoring,’ highlighting the need for more structured support through diverse scaffolding techniques. Based on these findings, the pedagogical implications of using AI as a metacognitive scaffolding tool in language learning emphasize the need for educators to carefully integrate AI tools into the learning process. This integration should ensure that AI not only promote learners’ awareness of their learning but also encourage active monitoring and regulation of their learning strategies.
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